{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\"]", "output": "[['wait', 'service general', 'negative', 'long'], ['atmosphere', 'ambience general', 'negative', 'raucous'], ['NULL', 'restaurant miscellaneous', 'negative', 'sleek']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Just because it 's cheap does NOT mean the portions are small or the food is nasty , IT IS GREAT !\"]", "output": "[['food', 'food quality', 'positive', 'GREAT'], ['NULL', 'restaurant prices', 'positive', 'cheap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food is excellent .']", "output": "[['Food', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['As always we had a great glass of wine while we waited .']", "output": "[['glass of wine', 'drinks quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I can not imagine a friendlier staff working in a restaurant .']", "output": "[['staff', 'service general', 'positive', 'friendlier']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Also , specify if you like your food spicy- its rather bland if you do n't .\"]", "output": "[['food', 'food quality', 'negative', 'bland']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Big Wong gets big Ups for a fine establishment .']", "output": "[['Big Wong', 'restaurant general', 'positive', 'big Ups'], ['Big Wong', 'restaurant general', 'positive', 'fine']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was pleasantly suprised .']", "output": "[['NULL', 'restaurant general', 'positive', 'pleasantly suprised']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We all agreed that mare is one of the best seafood restaurants in New York .']", "output": "[['mare', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I ca n't wait to go back .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place was nice and calm .']", "output": "[['place', 'ambience general', 'positive', 'nice'], ['place', 'ambience general', 'positive', 'calm']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Add to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .']", "output": "[['service', 'service general', 'positive', 'great'], ['food', 'food quality', 'positive', 'great'], ['food', 'food prices', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would highly recommend .']", "output": "[['NULL', 'restaurant general', 'positive', 'highly recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"DO not try unless you 're just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'Do not try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Very cozy and warm inside ...']", "output": "[['NULL', 'ambience general', 'positive', 'cozy'], ['NULL', 'ambience general', 'positive', 'warm']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .']", "output": "[['wines', 'drinks quality', 'negative', 'above average'], ['wines', 'drinks prices', 'negative', 'above average']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Kind , attentive wait staff .']", "output": "[['wait staff', 'service general', 'positive', 'Kind'], ['wait staff', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['All of the pizzas are terrific and the price is even better !']", "output": "[['pizzas', 'food quality', 'positive', 'terrific'], ['NULL', 'restaurant prices', 'positive', 'even better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's somewhere you can eat and be happy .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'happy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff was the friendliest that have seen in New York .']", "output": "[['staff', 'service general', 'positive', 'friendliest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Drinks way over priced .']", "output": "[['Drinks', 'drinks prices', 'negative', 'over priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sushi was awful !']", "output": "[['sushi', 'food quality', 'negative', 'awful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sandwiches are dry , tasteless and way overpriced .']", "output": "[['sandwiches', 'food quality', 'negative', 'dry'], ['sandwiches', 'food quality', 'negative', 'tasteless'], ['sandwiches', 'food prices', 'negative', 'overpriced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is very overpriced and not very tasty .']", "output": "[['NULL', 'food quality', 'negative', 'not very tasty'], ['NULL', 'food prices', 'negative', 'overpriced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Worth the trip from Manhattan .']", "output": "[['NULL', 'restaurant general', 'positive', 'Worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I like the somosas , chai , and the chole , but the dhosas and dhal were kinda dissapointing .']", "output": "[['somosas', 'food quality', 'positive', 'like'], ['chai', 'food quality', 'positive', 'like'], ['chole', 'food quality', 'positive', 'like'], ['dhosas', 'food quality', 'negative', 'dissapointing'], ['dhal', 'food quality', 'negative', 'dissapointing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"We wo n't go to this place again for a good meal .\"]", "output": "[['meal', 'food quality', 'negative', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Taxan delicious !']", "output": "[['Taxan', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['MMmmm ... it was delicious .']", "output": "[['NULL', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .']", "output": "[['hanger steak', 'food quality', 'negative', 'rubber'], ['tuna', 'food quality', 'negative', 'flavorless']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have been to Casimir over 5 times and I have always had a great time there .']", "output": "[['Casimir', 'restaurant general', 'positive', 'great time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is great .']", "output": "[['place', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The restaurant is a bit noisy but that is something that can be overlooked once you sit down and enjoy a great meal']", "output": "[['meal', 'food quality', 'positive', 'enjoy'], ['meal', 'food quality', 'positive', 'great'], ['restaurant', 'ambience general', 'negative', 'noisy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Their bagels are fine , but they are a little overcooked , and not really a 'special ' bagel experience .\"]", "output": "[['bagels', 'food quality', 'negative', 'fine'], ['bagels', 'food quality', 'negative', 'overcooked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Have eaten at Ginger House several times , and it 's always good .\"]", "output": "[['Ginger House', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !']", "output": "[['people', 'service general', 'negative', 'evil'], ['people', 'service general', 'negative', 'incompetent'], ['Cafe Noir', 'restaurant general', 'positive', 'like']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A cool bar with great food , and tons of excellent beer .']", "output": "[['bar', 'ambience general', 'positive', 'cool'], ['food', 'food quality', 'positive', 'great'], ['beer', 'drinks quality', 'positive', 'excellent'], ['beer', 'drinks style_options', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Do n't dine at Tamarind for the vegetarian dishes , they are simply not up to par with the non-veg selections .\"]", "output": "[['vegetarian dishes', 'food quality', 'negative', 'not up to par']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not the typical NYC gimmick theme restaurant .']", "output": "[['restaurant', 'ambience general', 'positive', 'Not the typical']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ravioli was good ... but I have to say that I found everything a bit overpriced .']", "output": "[['Ravioli', 'food quality', 'positive', 'good'], ['NULL', 'food prices', 'negative', 'overpriced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Other guests enjoyed pizza , santa fe chopped salad and fish and chips .']", "output": "[['pizza', 'food quality', 'positive', 'enjoyed'], ['santa fe chopped salad', 'food quality', 'positive', 'enjoyed'], ['fish and chips', 'food quality', 'positive', 'enjoyed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They have it all -- great price , food , and service .']", "output": "[['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Most of the servers are very attentive , friendly and quite attractive .']", "output": "[['servers', 'service general', 'positive', 'attentive'], ['servers', 'service general', 'positive', 'friendly'], ['servers', 'service general', 'positive', 'attractive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Admittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .']", "output": "[['open kitchen', 'ambience general', 'positive', 'charm'], ['restaurant', 'ambience general', 'negative', 'warm']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['So rushing us out was absolutely unnecessary !']", "output": "[['NULL', 'service general', 'negative', 'rushing us out']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try green curry with vegetables .']", "output": "[['green curry with vegetables', 'food quality', 'positive', 'Try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service is good and the resturant is clean .']", "output": "[['service', 'service general', 'positive', 'good'], ['resturant', 'ambience general', 'positive', 'clean']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['What a great place !']", "output": "[['place', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were very pleasantly surprised .']", "output": "[['NULL', 'restaurant general', 'positive', 'pleasantly surprised']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great friendly service , Fast seating , Fast Delivery , Excellent sushi .']", "output": "[['service', 'service general', 'positive', 'Great friendly'], ['seating', 'service general', 'positive', 'Fast'], ['Delivery', 'service general', 'positive', 'Fast'], ['sushi', 'food quality', 'positive', 'Excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service is excellent , the decor is great , and the food is delicious and comes in large portions .']", "output": "[['service', 'service general', 'positive', 'excellent'], ['decor', 'ambience general', 'positive', 'great'], ['food', 'food quality', 'positive', 'delicious'], ['portions', 'food style_options', 'positive', 'large']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The restaurant has a Family feel , not least with regard to the portions which are enormous ; the veal alone could have single-handedly solved third world famine .']", "output": "[['restaurant', 'ambience general', 'positive', 'Family feel'], ['portions', 'food style_options', 'positive', 'enormous'], ['veal', 'food style_options', 'positive', 'have single-handedly solved third world famine']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['highly recommended .']", "output": "[['NULL', 'restaurant general', 'positive', 'highly recommended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was average to above-average ; the French Onion soup filling yet not overly impressive , and the desserts not brilliant in any way .']", "output": "[['food', 'food quality', 'positive', 'average to above-average'], ['French Onion soup', 'food quality', 'negative', 'not overly impressive'], ['desserts', 'food quality', 'negative', 'not brilliant']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['How do you rate home ?']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'home']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'm not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and I all enjoy Mizu very much ... and we 're repeat customers .\"]", "output": "[['Mizu', 'restaurant general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .']", "output": "[['crabmeat lasagna', 'food quality', 'positive', 'out of this world']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Truly the mark of an attentive waiter .']", "output": "[['waiter', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The first time the sushi was outstanding , the second time it was a little bland .']", "output": "[['sushi', 'food quality', 'positive', 'outstanding'], ['sushi', 'food quality', 'negative', 'bland']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The restaurant looks out over beautiful green lawns to the Hudson River and the Statue of Liberty .']", "output": "[['restaurant', 'location general', 'positive', 'beautiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is excellent !']", "output": "[['food', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not impressed with the food .']", "output": "[['food', 'food quality', 'negative', 'Not impressed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I ca n't wait for summer , when they serve outside on their gigantic patio .\"]", "output": "[['patio', 'ambience general', 'positive', 'gigantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I definitely wouldn 't go back .\"]", "output": "[['NULL', 'restaurant general', 'negative', \"wouldn 't go back\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I have been going there since it opened and I ca n't get enough .\"]", "output": "[['NULL', 'restaurant general', 'positive', \"ca n't get enough\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"This place , which is only a few months old , is perhaps Queens ' biggest secret !\"]", "output": "[['place', 'restaurant general', 'positive', \"Queens ' biggest secret\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Definately check it out ! ! !']", "output": "[['NULL', 'restaurant general', 'positive', 'check it out']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['but the service was a bit slow .']", "output": "[['service', 'service general', 'negative', 'slow']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was authentic .']", "output": "[['food', 'food quality', 'positive', 'authentic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The Steak Tartare is a great bet , they fix it for you at the table .']", "output": "[['Steak Tartare', 'food style_options', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['stick with the chicken , beef , and lamb dishes .']", "output": "[['chicken', 'food quality', 'positive', 'stick'], ['beef', 'food quality', 'positive', 'stick'], ['lamb dishes', 'food quality', 'positive', 'stick']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This little place definitely exceeded my expectations and you sure get a lot of food for your money .']", "output": "[['food', 'food style_options', 'positive', 'lot'], ['place', 'restaurant general', 'positive', 'exceeded my expectations'], ['food', 'food prices', 'positive', 'lot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great vibe , lots of people .']", "output": "[['vibe', 'ambience general', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine list is also really nice .']", "output": "[['wine list', 'drinks style_options', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The exotic food is beautifully presented and is a delight in delicious combinations .']", "output": "[['exotic food', 'food style_options', 'positive', 'beautifully presented'], ['exotic food', 'food quality', 'positive', 'delight']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The cream cheeses are out of this world and I love that coffee ! !']", "output": "[['cream cheeses', 'food quality', 'positive', 'out of this world'], ['coffee', 'drinks quality', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The hostess and the waitress were incredibly rude and did everything they could to rush us out .']", "output": "[['hostess', 'service general', 'negative', 'rude'], ['waitress', 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .']", "output": "[['jelly fish', 'food quality', 'positive', 'recommend'], ['drunken chicken', 'food quality', 'positive', 'recommend'], ['soupy dumplings', 'food quality', 'positive', 'recommend'], ['stir fry blue crab', 'food quality', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We even had a visit from the Manager who wanted to make sure we were enjoying ourselves .']", "output": "[['Manager', 'service general', 'positive', 'enjoying']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['honestly the worst sushi my husband and i had in our entire lives .']", "output": "[['sushi', 'food quality', 'negative', 'worst']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The atmosphere is n't the greatest , but I suppose that 's how they keep the prices down .\"]", "output": "[['atmosphere', 'ambience general', 'negative', \"is n't the greatest\"], ['NULL', 'restaurant prices', 'positive', 'keep the prices down']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good for dates or with friends .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'Good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Be sure to try the seasonal , and always delicious , specials .']", "output": "[['specials', 'food quality', 'positive', 'try'], ['specials', 'food quality', 'positive', 'seasonal'], ['specials', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .']", "output": "[['food', 'food quality', 'neutral', 'decent'], ['food', 'food quality', 'negative', 'not great'], ['lemon salad', 'food quality', 'negative', 'exception']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Seating is always prompt , though the restaurant does fill up in the evening .']", "output": "[['Seating', 'service general', 'positive', 'prompt']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service is top notch .']", "output": "[['Service', 'service general', 'positive', 'top notch']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The entertainment was great they have shows that go on through out the dinner .']", "output": "[['entertainment', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Highly recommended to all !']", "output": "[['NULL', 'restaurant general', 'positive', 'recommended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If you like spicy food get the chicken vindaloo .']", "output": "[['chicken vindaloo', 'food quality', 'positive', 'get']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food was good not great not worth the wait or another visit']", "output": "[['Food', 'food quality', 'neutral', 'good not great not worth the wait or another visit']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Yes , the prices are high , but I felt it was worth it .']", "output": "[['NULL', 'restaurant prices', 'negative', 'high'], ['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I thought this place was totally overrated .']", "output": "[['place', 'restaurant general', 'negative', 'overrated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Have frequented 'ino for several years and the food remains excellent .\"]", "output": "[['food', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Cute place , nice wait staff but would never go there again .']", "output": "[['wait staff', 'service general', 'positive', 'nice'], ['place', 'ambience general', 'positive', 'Cute'], ['place', 'restaurant general', 'negative', 'never go']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We have been to this place many times , and always have great food , wine , and service .']", "output": "[['food', 'food quality', 'positive', 'great'], ['wine', 'drinks quality', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You can not go wrong with this place .']", "output": "[['place', 'restaurant general', 'positive', 'wrong']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Please take my advice , go and try this place .']", "output": "[['place', 'restaurant general', 'positive', 'go and try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is so much fun .']", "output": "[['place', 'ambience general', 'positive', 'fun']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only thing I moderately enjoyed was their Grilled Chicken special with Edamame Puree .']", "output": "[['Grilled Chicken special with Edamame Puree', 'food quality', 'positive', 'enjoyed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['To sum it up : Service varies from good to mediorce , depending on which waiter you get ; generally it is just average Ok .']", "output": "[['Service', 'service general', 'neutral', 'varies']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Love Pizza 33 ...']", "output": "[['Pizza 33', 'restaurant general', 'positive', 'Love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was bland oily .']", "output": "[['food', 'food quality', 'negative', 'bland oily']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was lousy - too sweet or too salty and the portions tiny .']", "output": "[['food', 'food quality', 'negative', 'lousy'], ['food', 'food quality', 'negative', 'too sweet'], ['food', 'food quality', 'negative', 'too salty'], ['portions', 'food style_options', 'negative', 'tiny']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Two complaints -- their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .']", "output": "[['appetizer selection', 'food style_options', 'negative', 'complaints']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We could have made a meal of the yummy dumplings from the dumpling menu .']", "output": "[['dumplings', 'food quality', 'positive', 'yummy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Hurley 's is like Cheers where everyone knows your name and they are ACTUALLY glad you came .\"]", "output": "[[\"Hurley 's\", 'service general', 'positive', 'glad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My wife and I always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .']", "output": "[['staff', 'service general', 'positive', 'enjoy'], ['staff', 'service general', 'positive', 'young'], ['staff', 'service general', 'positive', 'not always well trained'], ['staff', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"You ca n't go wrong with this place .\"]", "output": "[['place', 'restaurant general', 'positive', \"ca n't go wrong\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Personal pans are the perfect size for those hungry nights .']", "output": "[['Personal pans', 'food style_options', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Do not get the Go Go Hamburgers , no matter what the reviews say .']", "output": "[['Go Go Hamburgers', 'food quality', 'negative', 'Do not get']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would definitely recommend SEA if you like thai cuisine !']", "output": "[['thai cuisine', 'food quality', 'positive', 'like']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Also , the sandwiches ( nearing $ 7 ) did n't come with anything like chips or a side .\"]", "output": "[['sandwiches', 'food style_options', 'negative', \"did n't come with\"], ['sandwiches', 'food prices', 'negative', 'nearing $ 7 ']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ambience is delightful , service impeccable .']", "output": "[['Ambience', 'ambience general', 'positive', 'delightful'], ['service', 'service general', 'positive', 'impeccable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['For the people who want great food plus great service , Roxy is a place to AVOID !']", "output": "[['food', 'food quality', 'negative', 'great'], ['service', 'service general', 'negative', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The pizza was great .']", "output": "[['pizza', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My son and his girlfriend both wanted cheeseburgers and they were huge !']", "output": "[['cheeseburgers', 'food style_options', 'neutral', 'huge']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The counter service is bad .']", "output": "[['counter service', 'service general', 'negative', 'bad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Atmosphere is nice and relaxed too ...']", "output": "[['Atmosphere', 'ambience general', 'positive', 'nice'], ['Atmosphere', 'ambience general', 'positive', 'relaxed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was well prepared and the service impecable .']", "output": "[['food', 'food quality', 'positive', 'well prepared'], ['service', 'service general', 'positive', 'impecable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"One of us actually liked the expresso - that 's it .\"]", "output": "[['expresso', 'drinks quality', 'positive', 'liked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['In an area sadly lacking in decent Thai food , this is one of the best spots .']", "output": "[['Thai food', 'food quality', 'positive', 'decent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We had a good time .']", "output": "[['NULL', 'restaurant general', 'positive', 'good time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not sure where the previous reviewer , lonk , dined , but Saul is in a great neighborhood and has great food !']", "output": "[['neighborhood', 'location general', 'positive', 'great'], ['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['THe back garden sitting area is very pleasant , where you can see their personal herb garden .']", "output": "[['back garden sitting area', 'ambience general', 'positive', 'pleasant']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Also , because it is so thin , it gets cold very quickly and its not that filling .']", "output": "[['NULL', 'food quality', 'negative', 'gets cold very quickly'], ['NULL', 'food style_options', 'negative', 'not that filling']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food is great and inexpensive .']", "output": "[['Food', 'food quality', 'positive', 'great'], ['Food', 'food prices', 'positive', 'inexpensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['First of all Dal Bukhara Rocks .']", "output": "[['Dal Bukhara', 'food quality', 'positive', 'Rocks']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Decor is nice though service can be spotty .']", "output": "[['Decor', 'ambience general', 'positive', 'nice'], ['service', 'service general', 'negative', 'spotty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The location and ambience is Ok but the food is what makes up for it .']", "output": "[['location', 'location general', 'neutral', 'Ok'], ['ambience', 'ambience general', 'neutral', 'Ok'], ['food', 'food quality', 'positive', 'makes up for it']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I go and eat out at many different restaurants and this is one place you have go and try .']", "output": "[['place', 'restaurant general', 'positive', 'have go and try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food awesome .']", "output": "[['Food', 'food quality', 'positive', 'awesome']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['HIGHLY RECOMMENDED ! ! ! ! !']", "output": "[['NULL', 'restaurant general', 'positive', 'RECOMMENDED']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['good music , great food , speedy service affordable prices .']", "output": "[['music', 'ambience general', 'positive', 'good'], ['food', 'food quality', 'positive', 'great'], ['service', 'service general', 'positive', 'speedy'], ['NULL', 'restaurant prices', 'positive', 'affordable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Loved it !']", "output": "[['NULL', 'restaurant general', 'positive', 'Loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria 's .\"]", "output": "[['pizza', 'food quality', 'positive', 'delicious'], ['fresh mozzarella', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The characters really make for an enjoyable experience .']", "output": "[['characters', 'ambience general', 'positive', 'enjoyable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've never had bad service and the fish is fresh and delicious .\"]", "output": "[['service', 'service general', 'positive', 'never had bad'], ['fish', 'food quality', 'positive', 'fresh'], ['fish', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ambiance relaxed and stylish .']", "output": "[['Ambiance', 'ambience general', 'positive', 'relaxed'], ['Ambiance', 'ambience general', 'positive', 'stylish']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Moules were excellent , lobster ravioli was VERY salty !']", "output": "[['Moules', 'food quality', 'positive', 'excellent'], ['lobster ravioli', 'food quality', 'negative', 'salty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I wo n't go back unless someone else is footing the bill .\"]", "output": "[['NULL', 'restaurant prices', 'negative', \"wo n't go back\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['first it took us a long time to find the place .']", "output": "[['place', 'restaurant miscellaneous', 'negative', 'took us a long time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They refuse to seat parties of 3 or more on weekends .']", "output": "[['NULL', 'service general', 'negative', 'refuse']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['great for a romantic evening , or a fun evening with friends ...']", "output": "[['NULL', 'ambience general', 'positive', 'romantic'], ['NULL', 'restaurant miscellaneous', 'positive', 'fun']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['All in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .']", "output": "[['NULL', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They were served warm and had a soft fluffy interior .']", "output": "[['NULL', 'food quality', 'positive', 'warm'], ['NULL', 'food quality', 'positive', 'a soft fluffy interior']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Both times I was extremely dissappointed by the service , which was boarderline rude .']", "output": "[['service', 'service general', 'negative', 'dissappointed'], ['service', 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .']", "output": "[['menu', 'food style_options', 'negative', 'limited'], ['NULL', 'food quality', 'positive', 'ultra fresh'], ['NULL', 'food style_options', 'positive', 'a work of food art']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was also horrible and the ambience is not that great .']", "output": "[['Service', 'service general', 'negative', 'horrible'], ['ambience', 'ambience general', 'negative', 'not that great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Excellent dumplings served amid clean , chic decor .']", "output": "[['dumplings', 'food quality', 'positive', 'Excellent'], ['decor', 'ambience general', 'positive', 'clean'], ['decor', 'ambience general', 'positive', 'chic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I absolutely love this place ! ! !']", "output": "[['place', 'restaurant general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['As a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .']", "output": "[['Lucky Strike', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is wonderful , tasty and filling , and the service is professional and friendly .']", "output": "[['food', 'food quality', 'positive', 'wonderful'], ['food', 'food quality', 'positive', 'tasty'], ['food', 'food style_options', 'positive', 'filling'], ['service', 'service general', 'positive', 'professional'], ['service', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .']", "output": "[['service', 'service general', 'negative', 'terrible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My boyfriend had the New England Chowder it was good but I think the award should go to the Lobster Bisque .']", "output": "[['New England Chowder', 'food quality', 'positive', 'good'], ['Lobster Bisque', 'food quality', 'positive', 'award']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was very good , a great deal , and the place its self was great .']", "output": "[['food', 'food quality', 'positive', 'good'], ['food', 'food prices', 'positive', 'good'], ['place', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I plan on stopping by next week as well .']", "output": "[['NULL', 'restaurant general', 'positive', 'stopping by next week']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Big Wong is a great place to eat and fill your stomach .']", "output": "[['Big Wong', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was attentive .']", "output": "[['service', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['service is friendly , and never had a problem walking in and getting a table .']", "output": "[['service', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Authentic Pakistani food .']", "output": "[['Pakistani food', 'food quality', 'positive', 'Authentic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I ate here a week ago and found most dishes average at best and too expensive .']", "output": "[['dishes', 'food quality', 'negative', 'average'], ['dishes', 'food prices', 'negative', 'too expensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Ca n't wait to go back .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['These are overpriced and you can get better just around the corner :']", "output": "[['NULL', 'food prices', 'negative', 'overpriced'], ['NULL', 'food quality', 'negative', 'can get better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the turkey burgers are scary !']", "output": "[['turkey burgers', 'food quality', 'negative', 'scary']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A little overpriced but worth it once you take a bite .']", "output": "[['NULL', 'food prices', 'negative', 'overpriced'], ['NULL', 'food quality', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"If you do n't mind pre-sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\"]", "output": "[['fish', 'food quality', 'negative', 'low quality'], ['staff', 'service general', 'negative', 'unfriendly'], ['sushi chef', 'restaurant miscellaneous', 'negative', 'miserable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The rest of the dim sum , though pricey by Chinatown standards , is worth it .']", "output": "[['dim sum', 'food prices', 'negative', 'pricey'], ['dim sum', 'food quality', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If you are looking for a good quality , cheap eats - this is the place .']", "output": "[['eats', 'food quality', 'positive', 'good quality'], ['eats', 'food prices', 'positive', 'cheap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is delicious - from the specials to the regular menu-fare , the dishes are never a disappointment .']", "output": "[['food', 'food quality', 'positive', 'delicious'], ['dishes', 'food quality', 'positive', 'never a disappointment'], ['specials', 'food quality', 'positive', 'delicious'], ['regular menu-fare', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The prices were CHEAP compared to the quality of service and food .']", "output": "[['NULL', 'restaurant prices', 'positive', 'CHEAP']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Fantastic place .']", "output": "[['place', 'restaurant general', 'positive', 'Fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This tiny restaurant is as cozy as it gets , with that certain Parisian flair .']", "output": "[['restaurant', 'ambience general', 'positive', 'cozy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Took my mom for Mother 's Day , and the maitre was pretty rude .\"]", "output": "[[\"maitre\", 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If you want something really different than try Jekyll and Hyde .']", "output": "[['Jekyll and Hyde', 'restaurant general', 'positive', 'different']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sushi is amazing ! ! !']", "output": "[['sushi', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The fish was not fresh and the rice tasted old and stale .']", "output": "[['fish', 'food quality', 'negative', 'not fresh'], ['rice', 'food quality', 'negative', 'old'], ['rice', 'food quality', 'negative', 'stale']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['skip dessert .']", "output": "[['dessert', 'food quality', 'negative', 'skip']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is so cheap and the waiters are nice .']", "output": "[['food', 'food prices', 'positive', 'cheap'], ['waiters', 'service general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is a great place to take out-of-towners , and perfect for watching the sunset .']", "output": "[['place', 'location general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine list is interesting and has many good values .']", "output": "[['wine list', 'drinks style_options', 'positive', 'interesting'], ['wine list', 'drinks prices', 'positive', 'good values']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Suan is a great place that I often take my friends ( classmates ) too .']", "output": "[['Suan', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"If you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\"]", "output": "[['bottle', 'drinks prices', 'negative', 'love'], ['music', 'ambience general', 'negative', 'like']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We left , never to return .']", "output": "[['NULL', 'restaurant general', 'negative', 'never to return']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .']", "output": "[['anti-pasta', 'food quality', 'positive', 'excellent'], ['calamari', 'food quality', 'positive', 'excellent'], ['pasta mains', 'food quality', 'positive', 'excellent'], ['pasta mains', 'food style_options', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Of course , it is crowded but who cares .']", "output": "[['NULL', 'ambience general', 'neutral', 'crowded but who cares']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We all felt it was worth it .']", "output": "[['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We waited at the bar and had martinis that were just right .']", "output": "[['martinis', 'drinks quality', 'positive', 'right']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The portion sizes here are huge , and the sushi is good .']", "output": "[['portion sizes', 'food style_options', 'positive', 'huge'], ['sushi', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But the best part about LS is the late night atmosphere , delightfully free of the BTs .']", "output": "[['late night atmosphere', 'ambience general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have NEVER been disappointed in the Red Eye .']", "output": "[['Red Eye', 'restaurant general', 'positive', 'NEVER been disappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I went there for lunch and it was not as good as I expected from the reviews I read .']", "output": "[['lunch', 'food quality', 'negative', 'not as good as I expected']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .']", "output": "[['chicken pot pie', 'food quality', 'positive', 'exceptional'], ['cheeseburger', 'food style_options', 'positive', 'huge'], ['cheeseburger', 'food quality', 'positive', 'delictable'], ['service', 'service general', 'positive', 'professional'], ['service', 'service general', 'positive', 'warm']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['With the theater 2 blocks away we had a delicious meal in a beautiful room .']", "output": "[['meal', 'food quality', 'positive', 'delicious'], ['room', 'ambience general', 'positive', 'beautiful'], ['NULL', 'location general', 'positive', '2 blocks away']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try the lobster teriyaki and the rose special roll .']", "output": "[['lobster teriyaki', 'food quality', 'positive', 'Try'], ['rose special roll', 'food quality', 'positive', 'Try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place has got to be the best japanese restaurant in the new york area .']", "output": "[['place', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have never before eaten 40 pieces of relatively good nigiri .']", "output": "[['nigiri', 'food quality', 'neutral', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I did n't complain , I liked the atmosphere so much .\"]", "output": "[['atmosphere', 'ambience general', 'positive', 'liked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great pizza for lunch place .']", "output": "[['pizza', 'food quality', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Someone else recommended the dessert - we also left that .']", "output": "[['dessert', 'food quality', 'negative', 'recommended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best Italian food I ever had ( and being Italian , that means alot ) .']", "output": "[['Italian food', 'food quality', 'positive', 'Best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My Girlfriend and I stumbled onto this hopping place the other night and had a great time !']", "output": "[['place', 'restaurant general', 'positive', 'hopping'], ['place', 'restaurant general', 'positive', 'great time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['So close , but not good enough .']", "output": "[['NULL', 'restaurant general', 'neutral', 'not good enough']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food , good size menu , great service and an unpretensious setting .']", "output": "[['food', 'food quality', 'positive', 'Great'], ['menu', 'food style_options', 'positive', 'good size'], ['service', 'service general', 'positive', 'great'], ['setting', 'ambience general', 'positive', 'unpretensious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The shrimp scampi was excellent and the antipasti were plentiful .']", "output": "[['shrimp scampi', 'food quality', 'positive', 'excellent'], ['antipasti', 'food style_options', 'positive', 'plentiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Will absolutely visit again .']", "output": "[['NULL', 'restaurant general', 'positive', 'visit again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food arrived 20 minutes after I called , cold and soggy .']", "output": "[['food', 'food quality', 'negative', 'cold'], ['food', 'food quality', 'negative', 'soggy'], ['NULL', 'service general', 'negative', '20 minutes']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I recently went to this restaurant with some co-workers for lunch and had an amazing time .']", "output": "[['restaurant', 'restaurant general', 'positive', 'amazing time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've been there three times and have always had wonderful experiences .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is thick and slightly soggy .']", "output": "[['NULL', 'food quality', 'negative', 'thick'], ['NULL', 'food quality', 'negative', 'soggy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were worried we would have trouble getting in , but somehow managed to have a short wait .']", "output": "[['wait', 'service general', 'positive', 'short']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"But after last night , Spice Grill is the only place I 'm eating indian cuisine .\"]", "output": "[['indian cuisine', 'food quality', 'positive', 'the only place']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This was a great surprise .']", "output": "[['NULL', 'restaurant general', 'positive', 'a great surprise']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were planning to get dessert but the waitress basically through the bill at us before we had a chance to order .']", "output": "[['waitress', 'service general', 'negative', 'through the bill']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is the perfect date spot for Williamsburg couples .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Went there with my wife and we had to wait for a table even though you could see there many that were empty with not reservation sigh on them .']", "output": "[['NULL', 'service general', 'negative', 'wait']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wonderful strawberry daiquiries as well !']", "output": "[['strawberry daiquiries', 'drinks quality', 'positive', 'Wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They were such a rip-off ( $ 8 .95 for four small meat patties in steamed buns ) and not worth trying .']", "output": "[['NULL', 'food quality', 'negative', 'rip-off'], ['NULL', 'food style_options', 'negative', 'small'], ['NULL', 'food prices', 'negative', 'not worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But the staff was so horrible to us .']", "output": "[['staff', 'service general', 'negative', 'horrible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The hostess is rude to the point of being offensive .']", "output": "[['hostess', 'service general', 'negative', 'rude'], ['hostess', 'service general', 'negative', 'offensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've also been amazed at all the new additions in the past few years : A new Jazz Bar , the most fantastic Dining Garden , the Best Thin Crust Pizzas , and now a Lasagna Menu which is to die for ( these are not your average lasagnas ) !\"]", "output": "[['Dining Garden', 'ambience general', 'positive', 'fantastic'], ['Jazz Bar', 'ambience general', 'positive', 'new'], ['Thin Crust Pizzas', 'food quality', 'positive', 'Best'], ['Lasagna Menu', 'food quality', 'positive', 'die for']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A must try !']", "output": "[['NULL', 'restaurant general', 'positive', 'A must try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not a great place for family or general dining .']", "output": "[['place', 'restaurant miscellaneous', 'negative', 'Not a great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I can not imagine better Indian food in all of the city .']", "output": "[['Indian food', 'food quality', 'positive', 'better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I did not try the caviar but I tried their salmon and crab salad ( they are all good )']", "output": "[['salmon', 'food quality', 'positive', 'good'], ['crab salad', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We went here for lunch a couple of weeks ago on a Saturday , and I was thoroughly impressed with the food .']", "output": "[['food', 'food quality', 'positive', 'impressed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The location is perfect .']", "output": "[['location', 'location general', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A great place to meet up for some food and drinks ...']", "output": "[['place', 'restaurant miscellaneous', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Have been dozens of times and never failed to enjoy the experience .']", "output": "[['NULL', 'restaurant general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Planet Thai is great !']", "output": "[['Planet Thai', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not worth it .']", "output": "[['NULL', 'food prices', 'negative', 'Not worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['After really enjoying ourselves at the bar we sat down at a table and had dinner .']", "output": "[['bar', 'restaurant miscellaneous', 'positive', 'enjoying']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food is great .']", "output": "[['Food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This dish is my favorite and I always get it when I go there and never get tired of it .']", "output": "[['dish', 'food quality', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You should pass on the calamari .']", "output": "[['calamari', 'food quality', 'negative', 'pass']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The view is breathtaking the service is top notch ... the ambiance is wonderful .']", "output": "[['view', 'location general', 'positive', 'breathtaking'], ['service', 'service general', 'positive', 'top notch'], ['ambiance', 'ambience general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've been many time and have never been disappointed .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'never been disappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best drumsticks over rice and sour spicy soup in town !']", "output": "[['drumsticks over rice', 'food quality', 'positive', 'Best'], ['sour spicy soup', 'food quality', 'positive', 'Best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the drinks are amazing and half off till 8pm .']", "output": "[['drinks', 'drinks quality', 'positive', 'amazing'], ['drinks', 'drinks prices', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'm glad I was introduced to this place and this is a rare gem in NY .\"]", "output": "[['place', 'restaurant general', 'positive', 'glad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['JUST AWSOME .']", "output": "[['NULL', 'food quality', 'positive', 'AWSOME']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You are not eating haut cuisine with subtle hints of whatever but : Cassuolet , Steake Fritte , Tripe Stew , etc ; simple stuff .']", "output": "[['NULL', 'food style_options', 'positive', 'simple'], ['NULL', 'food quality', 'positive', 'subtle hints of whatever']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"those rolls were big , but not good and sashimi was n't fresh .\"]", "output": "[['rolls', 'food style_options', 'positive', 'big'], ['rolls', 'food quality', 'negative', 'not good'], ['sashimi', 'food quality', 'negative', \"was n't fresh\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['THe perfect spot .']", "output": "[['spot', 'restaurant general', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good spreads , great beverage selections and bagels really tasty .']", "output": "[['spreads', 'food quality', 'positive', 'Good'], ['beverage selections', 'drinks style_options', 'positive', 'great'], ['bagels', 'food quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The blond wood decor is very soothing , the premium sake is excellent and the service is great .']", "output": "[['blond wood decor', 'ambience general', 'positive', 'soothing'], ['premium sake', 'drinks quality', 'positive', 'soothing'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was delicious ( I had a halibut special , my husband had steak ) , and the service was top-notch .']", "output": "[['food', 'food quality', 'positive', 'delicious'], ['halibut special', 'food quality', 'positive', 'delicious'], ['steak', 'food quality', 'positive', 'delicious'], ['service', 'service general', 'positive', 'top-notch']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"If you venture off the island of Manhattan and ca n't seem to find a great Italian restaurant , drive to Corona .\"]", "output": "[['Corona', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Try the Pad Thai , it 's fabulous and their prices are so cheap !\"]", "output": "[['Pad Thai', 'food quality', 'positive', 'Try'], ['Pad Thai', 'food quality', 'positive', 'fabulous'], ['NULL', 'restaurant prices', 'positive', 'cheap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I loved it and would go again .']", "output": "[['NULL', 'restaurant general', 'positive', 'loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Furthermore , the rice had no seasoning , so the sushi was bland and disgusting .']", "output": "[['rice', 'food quality', 'negative', 'no seasoning'], ['sushi', 'food quality', 'negative', 'bland'], ['sushi', 'food quality', 'negative', 'disgusting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everyone was more then happy with his choices .']", "output": "[['NULL', 'service general', 'positive', 'happy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The food is good , I ca n't lie .\"]", "output": "[['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .']", "output": "[['service', 'service general', 'positive', 'fast'], ['service', 'service general', 'positive', 'friendly'], ['food', 'food quality', 'positive', 'tasty'], ['hot sauce', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waiter was attentive .']", "output": "[['waiter', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['wont come back again for sure !']", "output": "[['NULL', 'restaurant general', 'negative', 'wont come back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I loved this place ! !']", "output": "[['place', 'restaurant general', 'positive', 'loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Just straight up cheap , good food .']", "output": "[['food', 'food quality', 'positive', 'good'], ['food', 'food prices', 'positive', 'cheap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had a huge pastrami sandwich on a roll .']", "output": "[['pastrami sandwich on a roll', 'food style_options', 'neutral', 'huge']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My husbands was perfect , my was well done and dry .']", "output": "[['NULL', 'food quality', 'positive', 'perfect'], ['NULL', 'food quality', 'negative', 'well done'], ['NULL', 'food quality', 'negative', 'dry']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .']", "output": "[['food', 'food quality', 'positive', 'amazing'], ['service', 'service general', 'positive', 'prompt'], ['service', 'service general', 'positive', 'helpful'], ['service', 'service general', 'positive', 'not over-bearing or rushed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['By far , the best pizza in Manhattan .']", "output": "[['pizza', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\"]", "output": "[['people', 'service general', 'positive', 'beautiful'], ['food', 'food quality', 'negative', \"does n't quite match up\"], ['Thalia', 'ambience general', 'positive', 'beautiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I wish they would change back to what it was before .']", "output": "[['NULL', 'restaurant general', 'negative', 'change back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The price very reasonable .']", "output": "[['NULL', 'restaurant prices', 'positive', 'reasonable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Went on a 3 day oyster binge , with Fish bringing up the closing , and I am so glad this was the place it O trip ended , because it was so great !']", "output": "[['oyster binge', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They even scoop it out nice ( for those on a diet ) not too much not to little .']", "output": "[['NULL', 'food style_options', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had a huge group for my birthday and we were well taken care of .']", "output": "[['NULL', 'service general', 'positive', 'well taken care of']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is simply amazing .']", "output": "[['NULL', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You can not go wrong at the Red Eye Grill .']", "output": "[['Red Eye Grill', 'restaurant general', 'positive', 'can not go wrong']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waitress was very patient with us and the food is phenomenal !']", "output": "[['waitress', 'service general', 'positive', 'patient'], ['food', 'food quality', 'positive', 'phenomenal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"We did n't want a bottle of bubbly on a weekday so we each got little bottles of Korbett it was just enough .\"]", "output": "[['bottles of Korbett', 'drinks style_options', 'positive', 'enough']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .']", "output": "[['chicken casserole', 'food quality', 'negative', 'all dark meat and on the bone '], ['chicken casserole', 'food style_options', 'negative', 'small']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .']", "output": "[['braised lamb shank in red wine', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['And the Tom Kha soup was pathetic .']", "output": "[['Tom Kha soup', 'food quality', 'negative', 'pathetic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['too large for just two people but nothing was left .']", "output": "[['NULL', 'food style_options', 'negative', 'too large'], ['NULL', 'food quality', 'positive', 'nothing was left']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Mizu is home to creative and unique rolls not to found anywhere else .']", "output": "[['rolls', 'food style_options', 'positive', 'unique']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Pizza here is consistently good .']", "output": "[['Pizza', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The decor is night tho ... but they REALLY need to clean that vent in the ceiling ... its quite un-appetizing , and kills your effort to make this place look sleek and modern .']", "output": "[['ceiling', 'ambience general', 'negative', 'un-appetizing'], ['vent', 'ambience general', 'negative', 'un-appetizing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The dinner was ok , nothing I would have again .']", "output": "[['dinner', 'food quality', 'negative', 'ok']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The prices are wonderfully low .']", "output": "[['NULL', 'restaurant prices', 'positive', 'wonderfully low']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Went here last night - nice decor , good service , but the food was surprisingly excellent .']", "output": "[['decor', 'ambience general', 'positive', 'nice'], ['service', 'service general', 'positive', 'good'], ['food', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Kind of a small place but I guess if they are not too busy might be able to fit a group or kids .']", "output": "[['place', 'restaurant miscellaneous', 'neutral', 'small']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A great choice at any cost and a great deal .']", "output": "[['NULL', 'restaurant general', 'positive', 'A great choice'], ['NULL', 'restaurant prices', 'positive', 'a great deal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I felt as though I were eating in Paris .']", "output": "[['NULL', 'food quality', 'positive', 'eating in Paris']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .']", "output": "[['waitstaff', 'service general', 'positive', 'comfortable'], ['waitstaff', 'service general', 'positive', 'relaxed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is the BEST Shabu-Shabu Restaurant in the Try-State Area .']", "output": "[['Shabu-Shabu Restaurant', 'restaurant general', 'positive', 'BEST']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But that is highly forgivable .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'highly forgivable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The crust is thin , the ingredients are fresh and the staff is friendly .']", "output": "[['crust', 'food quality', 'positive', 'thin'], ['staff', 'service general', 'positive', 'friendly'], ['ingredients', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Always great service !']", "output": "[['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The spicy Tuna roll is huge and probably the best that I 've had at this price range .\"]", "output": "[['spicy Tuna roll', 'food style_options', 'positive', 'huge'], ['spicy Tuna roll', 'food quality', 'positive', 'best'], ['spicy Tuna roll', 'food prices', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Love YUKA .']", "output": "[['YUKA', 'restaurant general', 'positive', 'Love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The menu has so many fish items and oysters .']", "output": "[['menu', 'food style_options', 'positive', 'so many']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place was quiet and delightful .']", "output": "[['place', 'ambience general', 'positive', 'quiet'], ['place', 'ambience general', 'positive', 'delightful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Skip this restaurant , it 's a big disappointment .\"]", "output": "[['restaurant', 'restaurant general', 'negative', 'Skip'], ['restaurant', 'restaurant general', 'negative', 'disappointment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is outstanding and the service is quick , friendly and very professional .']", "output": "[['food', 'food quality', 'positive', 'outstanding'], ['service', 'service general', 'positive', 'quick'], ['service', 'service general', 'positive', 'friendly'], ['service', 'service general', 'positive', 'professional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Anybody who likes this place must be from a different planet , where greasy , dry and tasteless are complimentary .']", "output": "[['NULL', 'food quality', 'negative', 'greasy'], ['NULL', 'food quality', 'negative', 'dry'], ['NULL', 'food quality', 'negative', 'tasteless']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['we love th pink pony .']", "output": "[['pink pony', 'restaurant general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I thanked my friend who recommended me this restaurant and will certainly recommend it to others .']", "output": "[['restaurant', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I am not a vegetarian but , almost all the dishes were great .']", "output": "[['dishes', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everything was wonderful ; food , drinks , staff , mileau .']", "output": "[['food', 'food quality', 'positive', 'wonderful'], ['drinks', 'drinks quality', 'positive', 'wonderful'], ['staff', 'service general', 'positive', 'wonderful'], ['mileau', 'ambience general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .']", "output": "[['Traditional French decour', 'ambience general', 'positive', 'pleasant'], ['hall', 'ambience general', 'negative', 'noisy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is really trendi but they have forgotten about the most important part of a restaurant , the food .']", "output": "[['food', 'food quality', 'negative', 'forgotten'], ['place', 'ambience general', 'positive', 'trendi']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was also very good .']", "output": "[['Service', 'service general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I went to Areo on a Sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .']", "output": "[['Areo', 'restaurant general', 'positive', 'enjoyable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had the duck breast special on my last visit and it was incredible .']", "output": "[['duck breast special', 'food quality', 'positive', 'incredible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The place 's decor and hidden bathrooms made for a good laugh .\"]", "output": "[['decor', 'ambience general', 'positive', 'good laugh'], ['hidden bathrooms', 'ambience general', 'positive', 'good laugh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Who has room for Cheesesticks with the best pizza in NYC !']", "output": "[['pizza', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['While it is understandable that every place is indeed different , there was not a need to be uncourteous to customers and downright rude .']", "output": "[['NULL', 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The rice to fish ration was also good -- they did n't try to overpack the rice .\"]", "output": "[['rice to fish ration', 'food style_options', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It hits the spot every time']", "output": "[['NULL', 'restaurant general', 'positive', 'hits the spot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A little pricey but it really hits the spot on a Sunday morning !']", "output": "[['NULL', 'restaurant prices', 'negative', 'pricey '], ['NULL', 'restaurant general', 'positive', 'hits the spot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\"]", "output": "[['setting', 'ambience general', 'positive', 'intimate'], ['service', 'service general', 'positive', 'nice'], ['NULL', 'restaurant miscellaneous', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try the crunchy tuna , it is to die for .']", "output": "[['crunchy tuna', 'food quality', 'positive', 'Try'], ['crunchy tuna', 'food quality', 'positive', 'die for']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Their calzones are horrific , bad , vomit-inducing , YUCK .']", "output": "[['calzones', 'food quality', 'negative', 'horrific'], ['calzones', 'food quality', 'negative', 'bad'], ['calzones', 'food quality', 'negative', 'vomit-inducing'], ['calzones', 'food quality', 'negative', 'YUCK']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My chow fun and chow see was really bland and oily .']", "output": "[['chow fun and chow see', 'food quality', 'negative', 'bland'], ['chow fun and chow see', 'food quality', 'negative', 'oily']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lamb was tender so full of flavor , the dessert was divine ! !']", "output": "[['lamb', 'food quality', 'positive', 'tender'], ['dessert', 'food quality', 'positive', 'divine']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I am so coming back here again , as much as I can .']", "output": "[['NULL', 'restaurant general', 'positive', 'coming back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['On a hot day it was fabulous to stop in and enjoy lunch .']", "output": "[['NULL', 'restaurant general', 'positive', 'fabulous'], ['NULL', 'restaurant general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best of all is the warm vibe , the owner is super friendly and service is fast .']", "output": "[['vibe', 'ambience general', 'positive', 'warm'], ['owner', 'service general', 'positive', 'friendly'], ['service', 'service general', 'positive', 'fast']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['fresh restaurant was amazing ... ... .. food was delicious and of course fresh .']", "output": "[['fresh restaurant', 'restaurant general', 'positive', 'fresh'], ['fresh restaurant', 'restaurant general', 'positive', 'amazing'], ['food', 'food quality', 'positive', 'delicious'], ['food', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Go here for a romantic dinner but not for an all out wow dining experience .']", "output": "[['NULL', 'ambience general', 'positive', 'romantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Nobody at this restaurant will give firm answers about anything and in the end , not one person takes responsibility for anything .']", "output": "[['NULL', 'service general', 'negative', 'not one person takes responsibility for anything']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Definitely worth the trip to Battery Park City !']", "output": "[['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great spot , whether looking for a couple of drinks or quiet dinner .']", "output": "[['spot', 'restaurant general', 'positive', 'Great'], ['spot', 'restaurant miscellaneous', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service is good and ambience is good for a date or group outing .']", "output": "[['service', 'service general', 'positive', 'good'], ['ambience', 'ambience general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lava cake dessert was incredible and I recommend it .']", "output": "[['lava cake dessert', 'food quality', 'positive', 'incredible'], ['lava cake dessert', 'food quality', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only fallback on this restaurant is the prices .']", "output": "[['restaurant', 'restaurant prices', 'negative', 'fallback']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff there is very attentive and down to earth .']", "output": "[['staff', 'service general', 'positive', 'attentive'], ['staff', 'service general', 'positive', 'down to earth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place is a lot of fun .']", "output": "[['place', 'ambience general', 'positive', 'fun']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .']", "output": "[['salmon', 'food quality', 'negative', 'wasnt impressed'], ['chicken', 'food quality', 'positive', 'good'], ['servings for main entree', 'food general', 'negative', 'Small']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Somehow working the italian charm with constant mille grazie does not constitute proper service .']", "output": "[['service', 'service general', 'negative', 'not constitute proper']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I found it on a cold night , the perfect spot to warm up .']", "output": "[['spot', 'restaurant miscellaneous', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .']", "output": "[['fish', 'food quality', 'positive', 'quality'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Inside is a little cramped , but to be expected .']", "output": "[['NULL', 'ambience general', 'neutral', 'to be expected']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A real dissapointment .']", "output": "[['NULL', 'food quality', 'negative', 'dissapointment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Beautiful experience .']", "output": "[['NULL', 'restaurant general', 'positive', 'Beautiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The owner and staff are all Japanese as well and that adds to the entire ambiance .']", "output": "[['ambiance', 'ambience general', 'positive', 'adds']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food , great decor , great service .']", "output": "[['food', 'food quality', 'positive', 'Great'], ['decor', 'ambience general', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I am reluctant to write because I would not want my jem of a pizza place to become overcrowded .']", "output": "[['pizza place', 'restaurant miscellaneous', 'positive', 'overcrowded']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Highly recommended .']", "output": "[['NULL', 'restaurant general', 'positive', 'Highly recommended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Chennai Garden is my favorite Indian restaurant in the city .']", "output": "[['Chennai Garden', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Once you step into Cosette , you 're miraculously in a small , off-the-beaten path Parisian bistro .\"]", "output": "[['Cosette', 'ambience general', 'positive', 'off-the-beaten']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\"]", "output": "[['NULL', 'food style_options', 'negative', 'eaten several more'], ['portion', 'food style_options', 'positive', 'fine'], ['french fries', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['$ 20 for all you can eat sushi can not be beaten .']", "output": "[['all you can eat sushi', 'food prices', 'positive', 'beaten']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is reliable and the price is moderate .']", "output": "[['food', 'food quality', 'positive', 'reliable'], ['NULL', 'restaurant prices', 'neutral', 'moderate']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We will definitely go back .']", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I really liked this place .']", "output": "[['place', 'restaurant general', 'positive', 'liked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the food is decent .']", "output": "[['food', 'food quality', 'neutral', 'decent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I think that it is absolutely brilliant and well runned business operation .']", "output": "[['NULL', 'restaurant general', 'positive', 'brilliant']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend the restaurant based on our experience last night .']", "output": "[['restaurant', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The server was really cool and served us our food and drinks with a smile .']", "output": "[['server', 'service general', 'positive', 'cool']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .']", "output": "[['NULL', 'food quality', 'positive', 'delicious'], ['NULL', 'ambience general', 'positive', 'perfect quiet'], ['NULL', 'ambience general', 'positive', 'relaxing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Do n't miss Bloom 's on your next trip to Manhatten .\"]", "output": "[[\"Bloom 's\", 'restaurant general', 'positive', 'miss']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It was divine melts in your mouth .']", "output": "[['NULL', 'food quality', 'positive', 'divine']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I think I 've had some the best meals of my life at minnow .\"]", "output": "[['meals', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"We had Pam 's special fried fish and it was amazing .\"]", "output": "[[\"Pam 's special fried fish\", 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service is average .']", "output": "[['Service', 'service general', 'neutral', 'average']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Fantastic !']", "output": "[['NULL', 'restaurant general', 'positive', 'Fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Everything I had was good , and I 'm a eater .\"]", "output": "[['NULL', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ask for Usha , the nicest bartender in manhattan .']", "output": "[['Usha', 'service general', 'positive', 'nicest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food , great prices , great service .']", "output": "[['food', 'food quality', 'positive', 'Great'], ['NULL', 'restaurant prices', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Yakitori ( bbq meats ) is tasty too .']", "output": "[['Yakitori ( bbq meats )', 'food quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food here is rather good , but only if you like to wait for it .']", "output": "[['food', 'food quality', 'positive', 'good'], ['NULL', 'service general', 'negative', 'wait for it']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !']", "output": "[['crust', 'food quality', 'positive', 'best'], ['pizza', 'food quality', 'positive', 'should not have additional toppings']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ambience is so cute and quaint , good for business although we were there on vacation .']", "output": "[['Ambience', 'ambience general', 'positive', 'cute'], ['Ambience', 'ambience general', 'positive', 'quaint'], ['Ambience', 'ambience general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Even my Indian friend couldn 't believe how good and tasty everything was .\"]", "output": "[['NULL', 'food quality', 'positive', 'good'], ['NULL', 'food quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Quite simply it 's like stepping out of Manhattan and into a haven of tranquility .\"]", "output": "[['NULL', 'ambience general', 'positive', 'a haven of tranquility']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The fried dumplings are GREAT !']", "output": "[['fried dumplings', 'food quality', 'positive', 'GREAT']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When I lived upstate for a while I would buy freeze the bagels and they would still be better than any else .']", "output": "[['bagels', 'food quality', 'positive', 'better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lox is always fresh too .']", "output": "[['lox', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wait staff is blantently unappreciative of your business but its the best pie on the UWS !']", "output": "[['Wait staff', 'service general', 'negative', 'unappreciative'], ['pie', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We took advanatage of the half price sushi deal on saturday so it was well worth it .']", "output": "[['half price sushi deal', 'food quality', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['$ 6 and there is much tasty food , all of it fresh and continually refilled .']", "output": "[['food', 'food quality', 'positive', 'tasty'], ['food', 'food quality', 'positive', 'fresh'], ['food', 'food prices', 'positive', 'refilled']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My boyfriend and I went there to celebrate my birthday the other night and all I can say is that it was magnificent .']", "output": "[['NULL', 'restaurant general', 'positive', 'magnificent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The people that work there are always so friendly you forget you are in New York sometimes .']", "output": "[['people', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I go twice a month !']", "output": "[['NULL', 'restaurant general', 'positive', 'twice a month']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It was the first place we ate on our first trip to New York , and it will be the last place we stop as we head out of town on our next trip to New York .']", "output": "[['place', 'restaurant miscellaneous', 'positive', 'the last place we stop']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['limited menu , no-so-fresh ingredients , thinly-sliced fish , fall-apart rice .']", "output": "[['menu', 'food style_options', 'negative', 'limited'], ['ingredients', 'food quality', 'negative', 'no-so-fresh'], ['fish', 'food style_options', 'negative', 'thinly-sliced'], ['rice', 'food style_options', 'negative', 'fall-apart']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was there on sat . for my birthday and we had an excellent time .']", "output": "[['NULL', 'restaurant general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's one of our favorite places to eat in NY .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The dishes offered were unique , very tasty and fresh from the lamb sausages , sardines with biscuits , large whole shrimp to the amazing pistachio ice cream ( the best and freshest I 've ever had ) .\"]", "output": "[['dishes', 'food quality', 'positive', 'unique'], ['dishes', 'food quality', 'positive', 'tasty'], ['dishes', 'food quality', 'positive', 'fresh'], ['pistachio ice cream', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The menu is very limited - i think we counted 4 or 5 entrees .']", "output": "[['menu', 'food style_options', 'negative', 'limited']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Raga 's is a romantic , cozy restaurant .\"]", "output": "[[\"Raga 's\", 'ambience general', 'positive', 'romantic'], [\"Raga 's\", 'ambience general', 'positive', 'cozy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend the Sophia pizza .']", "output": "[['Sophia pizza', 'food quality', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine list is extensive and impressive .']", "output": "[['wine list', 'drinks style_options', 'positive', 'extensive'], ['wine list', 'drinks style_options', 'positive', 'impressive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Excellent atmosphere , delicious dishes good and friendly service .']", "output": "[['atmosphere', 'ambience general', 'positive', 'Excellent'], ['dishes', 'food quality', 'positive', 'delicious'], ['service', 'service general', 'positive', 'good'], ['service', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were greeted promptly by the waiter who was very nice and cordial .']", "output": "[['waiter', 'service general', 'positive', 'nice'], ['waiter', 'service general', 'positive', 'cordial']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'm partial to the Gnocchi .\"]", "output": "[['Gnocchi', 'restaurant general', 'positive', 'partial']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\"]", "output": "[['food', 'food quality', 'positive', 'recommend'], ['ambience', 'ambience general', 'positive', 'recommend'], ['service', 'service general', 'positive', 'recommend'], ['service', 'service general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Salads are a delicious way to begin the meal .']", "output": "[['Salads', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Amma is nothing special .']", "output": "[['Amma', 'restaurant general', 'neutral', 'nothing special']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My friends settled for rice dishes , but we came back the following day to try the dim sum , which was good ... not outstanding , but good .']", "output": "[['dim sum', 'food quality', 'neutral', 'good'], ['dim sum', 'food quality', 'neutral', 'not outstanding']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['For authentic Thai food , look no further than Toons .']", "output": "[['Thai food', 'food quality', 'positive', 'authentic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We both opted for a pasta dish and they were served timely and fresh .']", "output": "[['NULL', 'service general', 'positive', 'served timely'], ['pasta dish', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Never again !']", "output": "[['NULL', 'restaurant general', 'negative', 'Never']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['He served me an Uni Hand roll , which I never had before , and let me tell you ... IT WAS HEAVEN !']", "output": "[['Uni Hand roll', 'food quality', 'positive', 'HEAVEN']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were 4 and got the family size penne a la vodka which was tremendously gigantic portion ... a bucket of food literally .']", "output": "[['penne a la vodka', 'food style_options', 'positive', 'gigantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They were very abrupt with me when I called and actually claimed the food was late because they were out of rice .']", "output": "[['NULL', 'service general', 'negative', 'abrupt']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Downstairs lounge is always a good attraction']", "output": "[['Downstairs lounge', 'ambience general', 'positive', 'good attraction']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is a nice restaurant if you are looking for a good place to host an intimate dinner meeting with business associates .']", "output": "[['restaurant', 'restaurant miscellaneous', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They should have called it mascarpone with chocolate chips-good but a far cry from what the name implies .']", "output": "[['NULL', 'food quality', 'negative', 'a far cry']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was very prompt but slightly rushed .']", "output": "[['Service', 'service general', 'positive', 'prompt'], ['Service', 'service general', 'positive', 'rushed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My boyfriend had Prime Rib it was good .']", "output": "[['Prime Rib', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Consistently good Japanese Tapas .']", "output": "[['Japanese Tapas', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food and the prices are very reasonable .']", "output": "[['food', 'food quality', 'positive', 'Great'], ['NULL', 'restaurant prices', 'positive', 'reasonable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Always busy but fast moving .']", "output": "[['NULL', 'service general', 'positive', 'fast moving']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place itself is beautiful the bar scene seems to be happening .']", "output": "[['place', 'ambience general', 'positive', 'beautiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was excellent - friendly and attentive .']", "output": "[['service', 'service general', 'positive', 'excellent'], ['service', 'service general', 'positive', 'friendly'], ['service', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waitress , seems to be more concerned of looking good than actually waitressing .']", "output": "[['waitress', 'service general', 'negative', 'more concerned of looking good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is my first time writing a review for a restaurant because the food and service was excellent .']", "output": "[['food', 'food quality', 'positive', 'excellent'], ['service', 'service general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The bagel was huge .']", "output": "[['bagel', 'food style_options', 'positive', 'huge']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .']", "output": "[['scallops', 'food quality', 'positive', 'like'], ['mahi mahi ( on saffron risotto', 'food quality', 'positive', 'like']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was quick .']", "output": "[['Service', 'service general', 'positive', 'quick']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"it 's a perfect place to have a amazing indian food .\"]", "output": "[['indian food', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had their eggs benedict for brunch , which were the worst in my entire life , I tried removing the hollondaise sauce completely that was how failed it was .']", "output": "[['eggs benedict', 'food quality', 'negative', 'worst']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The freshest , best variety , and the fastest delivery .']", "output": "[['NULL', 'food quality', 'positive', 'freshest'], ['NULL', 'food style_options', 'positive', 'best variety'], ['delivery', 'service general', 'positive', 'fastest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Too bad the food was n't of the same heritage .\"]", "output": "[['food', 'food quality', 'negative', 'bad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Conveniently located too , being right on Bedford ave .']", "output": "[['NULL', 'location general', 'positive', 'Conveniently']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is one of my favorite restaurants and it is not to be missed .']", "output": "[['NULL', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would highly recommend it .']", "output": "[['NULL', 'restaurant general', 'positive', 'highly recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Leon is an East Village gem : casual but hip , with well prepared basic French bistro fare , good specials , a warm and lively atmosphere .']", "output": "[['Leon', 'ambience general', 'positive', 'casual'], ['Leon', 'ambience general', 'positive', 'hip'], ['specials', 'food quality', 'positive', 'good'], ['atmosphere', 'ambience general', 'positive', 'warm'], ['atmosphere', 'ambience general', 'positive', 'lively'], ['French bistro fare', 'food quality', 'positive', 'well prepared']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .']", "output": "[['Appetizers', 'food quality', 'neutral', 'ok'], ['waiter', 'service general', 'negative', 'poor'], ['potato stuff kanish', 'food quality', 'positive', 'try'], ['potato stuff kanish', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I take all my NYC guests to VT 's .\"]", "output": "[[\"VT 's\", 'restaurant miscellaneous', 'positive', 'take all my NYC guests']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\"]", "output": "[['seafood', 'food quality', 'positive', 'amazing'], ['wine list', 'drinks style_options', 'positive', 'good'], ['menu', 'food style_options', 'positive', 'ever-changing'], ['menu', 'food style_options', 'positive', 'great surprises']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"In any event , this is a place I 'll be sure to stop by again when I 'm in this part of town .\"]", "output": "[['place', 'restaurant general', 'positive', 'stop by again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They have a huge selection of different cream cheeses and all of their salads are great .']", "output": "[['salads', 'food quality', 'positive', 'great'], ['cream cheeses', 'food style_options', 'positive', 'huge'], ['cream cheeses', 'food style_options', 'positive', 'different']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It looked like shredded cheese partly done - still in strips .']", "output": "[['NULL', 'food quality', 'negative', 'shredded cheese partly done']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I loved everythig about it-especially the shows and actors .']", "output": "[['shows', 'ambience general', 'positive', 'loved'], ['actors', 'ambience general', 'positive', 'loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff is no nonsense .']", "output": "[['staff', 'service general', 'positive', 'no nonsense']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['And evaluated on those terms Pastis is simply wonderful .']", "output": "[['Pastis', 'restaurant general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We had the lobster sandwich and it was FANTASTIC .']", "output": "[['lobster sandwich', 'food quality', 'positive', 'FANTASTIC']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Beef noodle soup is good as well .']", "output": "[['Beef noodle soup', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lobster sandwich is $ 24 and although it was good it was not nearly enough to warrant that price .']", "output": "[['lobster sandwich', 'food quality', 'positive', 'good'], ['lobster sandwich', 'food prices', 'negative', 'not nearly enough to warrant that price']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The price is reasonable although the service is poor .']", "output": "[['NULL', 'restaurant prices', 'positive', 'reasonable'], ['service', 'service general', 'negative', 'poor']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A big disappointment , all around .']", "output": "[['NULL', 'restaurant general', 'negative', 'disappointment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"however , it 's the service that leaves a bad taste in my mouth .\"]", "output": "[['service', 'service general', 'negative', 'bad taste']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['However , go for the ambience , and consider the food just a companion for a trip across the world !']", "output": "[['food', 'food quality', 'neutral', 'a companion for a trip across the world ']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I really loved the different and inovated touch that 's the cheff gives to the food .\"]", "output": "[['cheff', 'food style_options', 'positive', 'loved'], ['cheff', 'food style_options', 'positive', 'inovated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Big thumbs up !']", "output": "[['NULL', 'restaurant general', 'positive', 'thumbs up']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is one of the best comfort food places in the city .']", "output": "[['NULL', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've waited over one hour for food .\"]", "output": "[['NULL', 'service general', 'negative', 'waited over one hour']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the pad se ew chicken was delicious , however the pad thai was far too oily .']", "output": "[['pad se ew chicken', 'food quality', 'positive', 'delicious'], ['pad thai', 'food quality', 'negative', 'oily']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine list is extensive and can easily hike up an otherwise reasonably priced meal .']", "output": "[['wine list', 'drinks style_options', 'positive', 'extensive'], ['meal', 'food prices', 'positive', 'reasonably priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were not dissappointed in the least bit by this little gem .']", "output": "[['NULL', 'restaurant general', 'positive', 'not dissappointed in the least bit']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was very good - prompt , attentive and non-intrusive .']", "output": "[['Service', 'service general', 'positive', 'good'], ['Service', 'service general', 'positive', 'prompt'], ['Service', 'service general', 'positive', 'attentive'], ['Service', 'service general', 'positive', 'non-intrusive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The baked clams octopus we shared as appetizers were the best we 've ever had ! !\"]", "output": "[['baked clams octopus', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I expected quite a bit more from such an expensive menu .']", "output": "[['menu', 'food prices', 'negative', 'expensive'], ['menu', 'food quality', 'negative', 'expensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service is not exactly five star , but thats not really a big deal .']", "output": "[['Service', 'service general', 'neutral', 'not exactly five star']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .']", "output": "[['Food', 'food quality', 'positive', 'good'], ['raw vegatables in side orders', 'food quality', 'negative', 'wondered about freshmess']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['By far the best salad I have had in a fast food restaurant .']", "output": "[['salad', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is prepared quickly and efficiently .']", "output": "[['NULL', 'service general', 'positive', 'quickly'], ['NULL', 'service general', 'positive', 'efficiently']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .']", "output": "[['Gulab Jamun ( dessert )', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wine list selection is good and wine-by-the-glass was generously filled to the top .']", "output": "[['Wine list selection', 'drinks style_options', 'positive', 'good'], ['wine-by-the-glass', 'drinks style_options', 'positive', 'generously filled']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good food .']", "output": "[['food', 'food quality', 'positive', 'Good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The tuna and wasabe potatoes are excellent .']", "output": "[['tuna', 'food quality', 'positive', 'excellent'], ['wasabe potatoes', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is worth an one-hour drive .']", "output": "[['place', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I recieved prompt service with a smile .']", "output": "[['service', 'service general', 'positive', 'prompt']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"they were dry and disgusting , i did n't even finish my first piece .\"]", "output": "[['NULL', 'food quality', 'negative', 'dry'], ['NULL', 'food quality', 'negative', 'disgusting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"its a little out of the way if you do n't live in the neighborhood , but definitely worth the trip from wherever you are .\"]", "output": "[['NULL', 'location general', 'negative', 'a little out of the way'], ['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Rude service , medicore food ... there are tons of restaurants in NY ... stay away from this one']", "output": "[['service', 'service general', 'negative', 'Rude'], ['food', 'food quality', 'neutral', 'medicore'], ['NULL', 'restaurant general', 'negative', 'stay away']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It is set far from the small street it 's on , and there is no traffic noise .\"]", "output": "[['NULL', 'location general', 'positive', 'no traffic noise']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I look forward to eating here again']", "output": "[['NULL', 'restaurant general', 'positive', 'look forward']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I loved it and would HIGHLY RECOMMEND .']", "output": "[['NULL', 'restaurant general', 'positive', 'loved'], ['NULL', 'restaurant general', 'positive', 'HIGHLY RECOMMEND']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['even the wine by the glass was good .']", "output": "[['wine by the glass', 'drinks quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ess-A-Bagel ( either by Sty-town or midtown ) is by far the best bagel in NY .']", "output": "[['bagel', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sangria was pretty tasty and good on a hot muggy day .']", "output": "[['sangria', 'drinks quality', 'positive', 'tasty'], ['sangria', 'drinks quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Authentic Taiwanese food that 's cheap ... what more could you ask for ?\"]", "output": "[['Taiwanese food', 'food quality', 'positive', 'Authentic'], ['Taiwanese food', 'food prices', 'positive', 'cheap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .']", "output": "[['in-house lady DJ', 'ambience general', 'positive', 'good taste']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's great to go for a quick lunch either alone or with a friend .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Priced at upper intermediate range .']", "output": "[['NULL', 'restaurant prices', 'negative', 'upper intermediate']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The dining room is quietly elegant with no music to shout over -- how refreshing !']", "output": "[['dining room', 'ambience general', 'positive', 'elegant'], ['dining room', 'ambience general', 'positive', 'refreshing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is a wonderful place on all stand points especially value ofr money .']", "output": "[['place', 'restaurant prices', 'positive', 'wonderful'], ['place', 'restaurant general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Go there to relax and feel like your somewhere else .']", "output": "[['NULL', 'ambience general', 'positive', 'relax']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"This was my frist time at Cafe St. Bart 's and I must say how delicious the food and the service was .\"]", "output": "[['food', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .']", "output": "[['Food', 'food quality', 'positive', 'good'], ['pork belly', 'food quality', 'negative', 'roasted a bit longer']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine the service was very good too .']", "output": "[['wine', 'drinks quality', 'positive', 'good'], ['service', 'service general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's cuz it 's so good you need to taste it ! ! !\"]", "output": "[['NULL', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Open late ( well as late as I ever got there and I 'm a night person )\"]", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'well']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only problem is that the manager is a complete incompetent .']", "output": "[['manager', 'service general', 'negative', 'incompetent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .']", "output": "[['raddichio', 'food quality', 'negative', 'bitter'], ['NULL', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service is awful .']", "output": "[['service', 'service general', 'negative', 'awful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .']", "output": "[['ambience', 'ambience general', 'positive', 'correct'], ['staff', 'service general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service is not what one would expect from a joint in this price category .']", "output": "[['Service', 'service general', 'negative', 'not what one would expect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Calling the place Hampton Chutney Co. does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .']", "output": "[['place', 'restaurant general', 'negative', 'unattractive'], ['room', 'ambience general', 'negative', 'unattractive'], ['clerks', 'service general', 'negative', 'unhelpful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Probably would not go again ...']", "output": "[['NULL', 'restaurant general', 'negative', 'not go again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My friend devoured her chicken and mashed potatos .']", "output": "[['chicken and mashed potatos', 'food quality', 'positive', 'devoured']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It melted in my little mouth and the perfect consistency-not too fishy , creamy , and slightly buttery .']", "output": "[['NULL', 'food quality', 'positive', 'perfect consistency'], ['NULL', 'food quality', 'positive', 'not too fishy'], ['NULL', 'food quality', 'positive', 'creamy'], ['NULL', 'food quality', 'positive', 'buttery']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were then shooed inside .']", "output": "[['NULL', 'service general', 'negative', 'shooed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Although we were looking for regular lettuce and some walnuts the salads we got were great .']", "output": "[['salads', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great bagels made the old-fashioned way .']", "output": "[['bagels', 'food quality', 'positive', 'Great'], ['bagels', 'food style_options', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's boring on the inside , and our sushi was pretty below average ... the tuna was soggy and the other rolls had no flavor .\"]", "output": "[['NULL', 'ambience general', 'negative', 'boring'], ['sushi', 'food quality', 'negative', 'below average'], ['tuna', 'food quality', 'negative', 'soggy'], ['rolls', 'food quality', 'negative', 'no flavor']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were seated outside and the waiter spilled red wine and hot tea on myself and my date .']", "output": "[['waiter', 'service general', 'negative', 'spilled']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Delivery is fast too .']", "output": "[['Delivery', 'service general', 'positive', 'fast']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My friend got the mushroom pizza which tasted better .']", "output": "[['mushroom pizza', 'food quality', 'positive', 'better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Go to Volare for 1st class service and terrific food .']", "output": "[['service', 'service general', 'positive', '1st class'], ['food', 'food quality', 'positive', 'terrific']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I found the food to be outstanding , particulary the salmon dish I had .']", "output": "[['food', 'food quality', 'positive', 'outstanding'], ['salmon dish', 'food quality', 'positive', 'outstanding']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .']", "output": "[['outdoor atmosphere', 'location general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My friend from Milan and myself were pleasantly surprised when we arrived and everyone spoke italian .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'pleasantly surprised']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"If you 've ever been along the river in Weehawken you have an idea of the top of view the chart house has to offer .\"]", "output": "[['view', 'location general', 'positive', 'the top of view the chart house has to offer']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try the sea bass .']", "output": "[['sea bass', 'food quality', 'positive', 'Try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When family came in he gave them apps to test their palets , and then ordered for them .']", "output": "[['NULL', 'service general', 'positive', 'ordered for them']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Go hungry and enjoy .']", "output": "[['NULL', 'restaurant general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the waitstaffs are nice though .']", "output": "[['waitstaffs', 'service general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was not fresh , the sauces were bland and very oily .']", "output": "[['food', 'food quality', 'negative', 'not fresh'], ['sauces', 'food quality', 'negative', 'bland'], ['sauces', 'food quality', 'negative', 'oily']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .']", "output": "[['service', 'service general', 'positive', 'good'], ['place', 'ambience general', 'positive', 'relaxing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['( The asparagus , truffle oil , parmesan bruschetta is a winner ! )']", "output": "[['asparagus , truffle oil , parmesan bruschetta', 'food quality', 'positive', 'winner']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great pizza and fantastic service .']", "output": "[['pizza', 'food quality', 'positive', 'Great'], ['service', 'service general', 'positive', 'fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\"]", "output": "[['place', 'restaurant general', 'positive', 'amazed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You must try the shrimp appetizers .']", "output": "[['shrimp appetizers', 'food quality', 'positive', 'try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I would recommend Roxy 's for that , but not for their food .\"]", "output": "[['food', 'food quality', 'negative', 'recommend'], ['NULL', 'food quality', 'negative', 'not for their food']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The pizza was pretty good and huge .']", "output": "[['pizza', 'food quality', 'positive', 'good'], ['pizza', 'food style_options', 'positive', 'huge']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The workers there also absolutely load the bagel with cream cheese ( gets a little messy ) .']", "output": "[['bagel', 'food style_options', 'negative', 'messy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['this little place has a cute interior decor and affordable city prices .']", "output": "[['interior decor', 'ambience general', 'positive', 'cute'], ['place', 'restaurant prices', 'positive', 'little']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away .\"]", "output": "[['pizza', 'food quality', 'positive', 'ashamed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .']", "output": "[['porcini mushroom pasta special', 'food quality', 'negative', 'tasteless'], ['seafood tagliatelle', 'food quality', 'negative', 'tasteless']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is okay and the prices here are mediocre .']", "output": "[['food', 'food quality', 'neutral', 'okay'], ['NULL', 'restaurant prices', 'neutral', 'mediocre']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is great and reasonably priced .']", "output": "[['food', 'food quality', 'positive', 'great'], ['food', 'food prices', 'positive', 'reasonably priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Slightly on the pricey side but worth it !']", "output": "[['NULL', 'restaurant prices', 'negative', 'pricey'], ['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had the best ravioli ever .']", "output": "[['ravioli', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It was pretty inexpensive too .']", "output": "[['NULL', 'restaurant prices', 'positive', 'inexpensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You will not be disapointed at all .']", "output": "[['NULL', 'restaurant general', 'positive', 'will not be disapointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Their pad penang is delicious and everything else is fantastic .']", "output": "[['pad penang', 'food quality', 'positive', 'delicious'], ['NULL', 'food quality', 'positive', 'fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Fresh ingredients and everything is made to order .']", "output": "[['ingredients', 'food quality', 'positive', 'Fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !\"]", "output": "[['place', 'ambience general', 'positive', 'romantic'], ['place', 'ambience general', 'positive', 'comfortable'], ['service', 'service general', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The dosas are skimpy , unattractive and drip with grease , and personally I 'd drink popcorn topping before I 'd eat another one of these .\"]", "output": "[['dosas', 'food style_options', 'negative', 'skimpy'], ['dosas', 'food quality', 'negative', 'unattractive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the last time i walked by it looked pretty empty . hmmm .']", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'empty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The bar is very well stocked with interesting beers and well priced wines .']", "output": "[['bar', 'drinks style_options', 'positive', 'well stocked'], ['beers', 'drinks style_options', 'positive', 'interesting'], ['wines', 'drinks prices', 'positive', 'well priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['After all that , they complained to me about the small tip .']", "output": "[['NULL', 'service general', 'negative', 'complained']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Very affordable and excellent ambient !']", "output": "[['ambient', 'ambience general', 'positive', 'excellent'], ['NULL', 'restaurant prices', 'positive', 'affordable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The mussles were the fishiest things I 've ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w/ chicken had bones in it ... It was disgusting .\"]", "output": "[['mussles', 'food quality', 'negative', 'fishiest'], ['seabass', 'food quality', 'negative', 'bland'], ['goat cheese salad', 'food quality', 'negative', 'missing'], ['penne w/ chicken', 'food quality', 'negative', 'disgusting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would go back .']", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I tend to judge a sushi restaurant by its sea urchin , which was heavenly at sushi rose .']", "output": "[['sea urchin', 'food quality', 'positive', 'heavenly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"They forgot a sandwich , did n't include plastic forks , and did n't include pita with the hummus platter .\"]", "output": "[['NULL', 'service general', 'negative', 'forgot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was prompt , friendly and great .']", "output": "[['Service', 'service general', 'positive', 'prompt'], ['Service', 'service general', 'positive', 'friendly'], ['Service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"This was a repeat visit and we 'll definitely be back again .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'be back again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .']", "output": "[['staff', 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['sometimes i get good food and ok service .']", "output": "[['food', 'food quality', 'positive', 'good'], ['service', 'service general', 'neutral', 'ok']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service friendly and attentive .']", "output": "[['Service', 'service general', 'positive', 'friendly'], ['Service', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My fav was the sassy lassi ...']", "output": "[['sassy lassi', 'drinks quality', 'positive', 'fav']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is always packed .']", "output": "[['place', 'ambience general', 'neutral', 'packed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I do n't think I would go again .\"]", "output": "[['NULL', 'restaurant general', 'negative', \"do n't think I would go again \"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A guaranteeed delight !']", "output": "[['NULL', 'restaurant general', 'positive', 'delight']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['For the price , you can not eat this well in Manhattan .']", "output": "[['NULL', 'restaurant prices', 'negative', 'can not eat this well']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Everytime I decide to try another place on the UES , I get angry that I did n't just go to Zucchero Pomodori .\"]", "output": "[['Zucchero Pomodori', 'restaurant general', 'positive', \"did n't just go to\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['After dinner the manager grabbed my boyfriend , asked him : Where are you from ... maybe you dont know how things work in America ... and in the end stormed away almost teareyed yelling that tips are the only thing they survive on .']", "output": "[['manager', 'service general', 'negative', 'yelling']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The filet mignon dish was superb !']", "output": "[['filet mignon dish', 'food quality', 'positive', 'superb']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was prompt and courteous .']", "output": "[['Service', 'service general', 'positive', 'prompt'], ['Service', 'service general', 'positive', 'courteous']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'm still mad that i had to pay for lousy food .\"]", "output": "[['food', 'food quality', 'negative', 'lousy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .']", "output": "[['lobster sandwich', 'food quality', 'positive', 'good'], ['spaghetti with Scallops and Shrimp', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Patroon features a nice cigar bar and has great staff .']", "output": "[['cigar bar', 'ambience general', 'positive', 'nice'], ['staff', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['There is no excuse for such lousy service !']", "output": "[['service', 'service general', 'negative', 'lousy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Seriously , this place kicks ass .']", "output": "[['place', 'restaurant general', 'positive', 'kicks ass']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was absolutely amazing ! !']", "output": "[['food', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Overall I would recommend it and go back again .']", "output": "[['NULL', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is an amazing place to try some roti rolls .']", "output": "[['roti rolls', 'food quality', 'positive', 'try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We love the food , drinks , and atmosphere !']", "output": "[['food', 'food quality', 'positive', 'love'], ['drinks', 'drinks quality', 'positive', 'love'], ['atmosphere', 'ambience general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Hats off to the chef .']", "output": "[['chef', 'food quality', 'positive', 'Hats off']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Okay-i do n't mind the oily part ( cause most are cooked that way ) but it was way too bland .\"]", "output": "[['NULL', 'food quality', 'negative', 'oily'], ['NULL', 'food quality', 'negative', 'bland']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is by far my favorite place in the neighborhood .']", "output": "[['place', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The portions are large and the servers always surprise us with a different starter .']", "output": "[['portions', 'food style_options', 'positive', 'large']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\"]", "output": "[['delivery guys', 'service general', 'negative', 'downside']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"They 're rude at times , and not very friendly .\"]", "output": "[['NULL', 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were less than impressed .']", "output": "[['NULL', 'restaurant general', 'negative', 'less than impressed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They charge different prices all the time .']", "output": "[['NULL', 'service general', 'negative', 'charge different prices']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is the most Japanese it can ever get .']", "output": "[['place', 'restaurant miscellaneous', 'positive', 'Japanese']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Waitstaff are very friendly .']", "output": "[['Waitstaff', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !']", "output": "[['NULL', 'service general', 'negative', 'not helpful in the least']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have to say I have never had a disapointing meal here .']", "output": "[['meal', 'food quality', 'positive', 'never had a disapointing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was attentive , yet discreet .']", "output": "[['service', 'service general', 'positive', 'attentive'], ['service', 'service general', 'positive', 'discreet']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A classic !']", "output": "[['NULL', 'food quality', 'positive', 'classic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waitress moved our table practically into the bathroom and when we asked to cancel our dinner orders because we did not want to eat sitting on the toilet , we were told no ...']", "output": "[['waitress', 'service general', 'negative', 'sitting on the toilet']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['And it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3-6 each ) .']", "output": "[['NULL', 'food prices', 'positive', 'reasonable'], ['congee', 'food prices', 'positive', 'no more than $ 3-6 each'], ['noodles', 'food prices', 'positive', 'no more than $ 3-6 each'], ['rice dishes', 'food prices', 'positive', 'no more than $ 3-6 each']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Make sure you have the Spicy Scallop roll ...']", "output": "[['Spicy Scallop roll', 'food quality', 'positive', 'Make sure']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\"]", "output": "[['food', 'food quality', 'positive', 'great'], ['food', 'food prices', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['During the course of the past 3 months , the chef and staff changed and it was not for the better .']", "output": "[['chef', 'food quality', 'negative', 'changed'], ['staff', 'service general', 'negative', 'changed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like .\"]", "output": "[['food', 'food prices', 'negative', 'pricey'], ['prixe fixe tasting menu', 'food quality', 'positive', 'greatest'], ['prixe fixe tasting menu', 'food prices', 'positive', 'greatest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['i happen to have a policy that goes along with a little bit of self-respect , which includes not letting a waiter intimidate me , i.e . make me feel bad asking for trivialities like water , or the check .']", "output": "[['waiter', 'service general', 'negative', 'bad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I got an excellent piece of cheesecake and we had several other nice pastries .']", "output": "[['cheesecake', 'food quality', 'positive', 'excellent'], ['pastries', 'food quality', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was friendly and the atmosphere was casual .']", "output": "[['service', 'service general', 'positive', 'friendly'], ['atmosphere', 'ambience general', 'neutral', 'casual']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Definitely a great spot for a nice occasion or date .']", "output": "[['spot', 'restaurant miscellaneous', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Staff is very accomodating .']", "output": "[['Staff', 'service general', 'positive', 'accomodating']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I asked for an open faced cheese sandwich and the manager basically told me to take my business elsewhere !']", "output": "[['manager', 'service general', 'negative', 'take my business elsewhere']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Rao 's has the best service and atmosphere in NYC .\"]", "output": "[['service', 'service general', 'positive', 'best'], ['atmosphere', 'ambience general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Consequently , their burgers fell apart in their hands and made such a mess that they did'nt feel like finishing them .\"]", "output": "[['burgers', 'food style_options', 'negative', 'fell apart']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I recommend it !']", "output": "[['NULL', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The seats are uncomfortable if you are sitting against the wall on wooden benches .']", "output": "[['seats', 'ambience general', 'negative', 'uncomfortable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was delicious but do not come here on a empty stomach .']", "output": "[['food', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .']", "output": "[['congee', 'food quality', 'positive', 'Try'], ['Ow Ley Soh', 'food quality', 'positive', 'delicious'], ['Ow Ley Soh', 'food quality', 'positive', 'sweet']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I lOVE THIS PLACE !']", "output": "[['PLACE', 'restaurant general', 'positive', 'lOVE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Volare virgins or weekly regulars , everyone gets treated the same and you ca n't ask for more than that when the service is this friendly .\"]", "output": "[['service', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I absolutely Loved this place .']", "output": "[['place', 'restaurant general', 'positive', 'Loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Saul is the best restaurant on Smith Street and in Brooklyn .']", "output": "[['Saul', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Fish was overdone .']", "output": "[['Fish', 'food quality', 'negative', 'overdone']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Excellent spot for holiday get togethers with co-workers or friends that you have n't seen in a while .\"]", "output": "[['spot', 'restaurant miscellaneous', 'positive', 'Excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I have tried to make reservations , but both times , the hostess did n't have my name .\"]", "output": "[['hostess', 'service general', 'negative', \"did n't have my name\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I also ordered the Change Mojito , which was out of this world .']", "output": "[['Change Mojito', 'drinks quality', 'positive', 'out of this world']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But too far east !']", "output": "[['NULL', 'location general', 'negative', 'too far']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\"]", "output": "[['back patio', 'ambience general', 'positive', 'worth'], ['back patio', 'ambience general', 'positive', 'cool'], ['music', 'ambience general', 'positive', 'well']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"There 's nice and quiet , small but enough for 6 ( or more ) .\"]", "output": "[['NULL', 'ambience general', 'positive', 'quiet'], ['NULL', 'ambience general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've lived in NY for 5 years and this place has it all .\"]", "output": "[['place', 'restaurant general', 'positive', 'has it all']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The Yellowtail was particularly good as well .']", "output": "[['Yellowtail', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The drinks are always welll made and wine selection is fairly priced .']", "output": "[['drinks', 'drinks quality', 'positive', 'welll made'], ['wine selection', 'drinks prices', 'positive', 'fairly priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ingredients are organic which is a real plus for me .']", "output": "[['Ingredients', 'food quality', 'positive', 'organic'], ['Ingredients', 'food quality', 'positive', 'plus']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Dessert is a joke ... dont bother']", "output": "[['Dessert', 'food quality', 'negative', 'joke']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Cheese plate is a varied delight and great bargain at $ 10 .']", "output": "[['Cheese plate', 'food quality', 'positive', 'varied delight'], ['Cheese plate', 'food style_options', 'positive', 'varied delight'], ['Cheese plate', 'food prices', 'positive', 'great bargain']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she 's-way-cuter-than-me-that-b @ # $ * way ) .\"]", "output": "[['wait staff', 'service general', 'positive', 'pleasant'], ['wait staff', 'service general', 'positive', 'fun'], ['wait staff', 'service general', 'positive', 'gorgeous']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great service , great food .']", "output": "[['service', 'service general', 'positive', 'Great'], ['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We concluded with tiramisu chocolate cake , both were delicious .']", "output": "[['tiramisu chocolate cake', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You will find yourself returning quite often .']", "output": "[['NULL', 'restaurant general', 'positive', 'returning quite often']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .']", "output": "[['sushi', 'food quality', 'positive', 'particular'], ['ceviche mix ( special )', 'food quality', 'positive', 'please'], ['crab dumplings', 'food quality', 'positive', 'please'], ['assorted sashimi', 'food quality', 'positive', 'please'], ['sushi', 'food quality', 'positive', 'particular'], ['rolls', 'food quality', 'positive', 'please'], ['two types of sake', 'drinks quality', 'positive', 'please'], ['banana tempura', 'food quality', 'positive', 'please']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's a small cute restaurant .\"]", "output": "[['restaurant', 'restaurant general', 'positive', 'small cute']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff offers impeccable service .']", "output": "[['staff', 'service general', 'positive', 'impeccable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Drawbacks : service is slow and they do n't toast !\"]", "output": "[['service', 'service general', 'negative', 'slow']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Our teenage kids love it , too .']", "output": "[['NULL', 'restaurant general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['THe Pizza and wine were excellent -- the service too -- but what really MADE this place was the backyard dining area .']", "output": "[['Pizza', 'food quality', 'positive', 'excellent'], ['wine', 'drinks quality', 'positive', 'excellent'], ['service', 'service general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\"]", "output": "[['wait staff', 'service general', 'positive', 'freindly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Amazing !']", "output": "[['NULL', 'food quality', 'positive', 'Amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They have authentic Indian at amazin prices .']", "output": "[['Indian', 'food quality', 'positive', 'authentic'], ['Indian', 'food prices', 'positive', 'authentic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"You ca n't go wrong here .\"]", "output": "[['NULL', 'restaurant general', 'positive', \"ca n't go wrong\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\"]", "output": "[['martini', 'drinks quality', 'positive', 'delish'], ['martini', 'drinks style_options', 'positive', 'delish'], ['martini', 'drinks prices', 'positive', 'delish'], ['Vanilla Shanty', 'drinks quality', 'positive', 'recommend'], ['setting', 'ambience general', 'positive', 'homey'], ['music', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Judging from previous posts this used to be a good place , but not any longer .']", "output": "[['place', 'restaurant general', 'negative', 'used to be a good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Even though its good seafood , the prices are too high .']", "output": "[['seafood', 'food quality', 'positive', 'good'], ['NULL', 'restaurant prices', 'negative', 'high']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I will definetly be going back .']", "output": "[['NULL', 'restaurant general', 'positive', 'going back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Worth visiting the 1st Ave spot because it is the original store .']", "output": "[['1st Ave spot', 'restaurant miscellaneous', 'positive', 'Worth visiting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['To me it exemplifies Soho , cute , artsy , interesting .']", "output": "[['NULL', 'ambience general', 'positive', 'cute'], ['NULL', 'ambience general', 'positive', 'artsy'], ['NULL', 'ambience general', 'positive', 'interesting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['( Always ask the bartender for the SEASONAL beer ! ! !']", "output": "[['SEASONAL beer', 'drinks quality', 'positive', 'Always ask']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Salads were fantastic .']", "output": "[['Salads', 'food quality', 'positive', 'fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Always a nice crowd , but never loud .']", "output": "[['crowd', 'restaurant miscellaneous', 'positive', 'nice'], ['NULL', 'ambience general', 'positive', 'never loud']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The large selection of bruschettas , paninis , tramezzinis keep the palate from stagnating .']", "output": "[['bruschettas', 'food style_options', 'positive', 'large selection'], ['paninis', 'food style_options', 'positive', 'large selection'], ['tramezzinis', 'food style_options', 'positive', 'large selection']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Give it a try and enjoy .']", "output": "[['NULL', 'restaurant general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .']", "output": "[['beers', 'drinks style_options', 'positive', 'happy'], ['food', 'food quality', 'positive', 'delicious'], ['pumkin tortelini', 'food quality', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They smell like they stuff them with old canned vegetables like the spinach mushroom calzone .']", "output": "[['NULL', 'food quality', 'negative', 'old canned vegetables']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Decent wine at reasonable prices .']", "output": "[['wine', 'drinks quality', 'positive', 'Decent'], ['wine', 'drinks prices', 'positive', 'reasonable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\"]", "output": "[['place', 'ambience general', 'negative', '*not* romantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If you go , try the marinara/arrabiatta sauce , the mozzarella en Carozza is mmmmmmmm ... .. everything is just delicious .']", "output": "[['marinara/arrabiatta sauce', 'food quality', 'positive', 'try'], ['marinara/arrabiatta sauce', 'food quality', 'positive', 'delicious'], ['mozzarella en Carozza', 'food quality', 'positive', 'delicious'], ['NULL', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Have been several times and it never dissapoints .']", "output": "[['NULL', 'restaurant general', 'positive', 'never dissapoints']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Quick and friendly service .']", "output": "[['service', 'service general', 'positive', 'Quick'], ['service', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not only is the cuisine the best around , the service has always been attentive and charming .']", "output": "[['cuisine', 'food quality', 'positive', 'best'], ['service', 'service general', 'positive', 'attentive'], ['service', 'service general', 'positive', 'charming']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Avoid this place !']", "output": "[['place', 'restaurant general', 'negative', 'Avoid']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is not worth the prices .']", "output": "[['place', 'restaurant general', 'negative', 'not worth the prices'], ['place', 'restaurant prices', 'negative', 'not worth the prices']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"For those that go once and do n't enjoy it , all I can say is that they just do n't get it .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The menu is limited but almost all of the dishes are excellent .']", "output": "[['menu', 'food style_options', 'negative', 'limited'], ['dishes', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The bagels always warm , soft on the inside , crispy on the outside and enormous in size .']", "output": "[['bagels', 'food quality', 'positive', 'warm'], ['bagels', 'food quality', 'positive', 'soft'], ['bagels', 'food quality', 'positive', 'crispy'], ['bagels', 'food style_options', 'positive', 'enormous']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff is incredibly helpful and attentive .']", "output": "[['staff', 'service general', 'positive', 'helpful'], ['staff', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I / we will never go back to this place again .']", "output": "[['place', 'restaurant general', 'negative', 'never go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Pizza was a little soggy .']", "output": "[['Pizza', 'food quality', 'negative', 'soggy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was good too .']", "output": "[['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['With so many poor experiences to be had in the theater district , is truly an excellent find !']", "output": "[['NULL', 'restaurant general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We thought that this place is using too much of MSG cooking in the foods .']", "output": "[['foods', 'food quality', 'negative', 'using too much of MSG']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have been going back again and again .']", "output": "[['NULL', 'restaurant general', 'positive', 'going back again and again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"it 's the only place you can get yummy authentic japanese comfort food .\"]", "output": "[['japanese comfort food', 'food quality', 'positive', 'yummy authentic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I will go back to Suan soon !']", "output": "[['Suan', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .']", "output": "[['outdoor seating', 'ambience general', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was actually aweful .']", "output": "[['food', 'food quality', 'negative', 'aweful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .']", "output": "[['caviar', 'food quality', 'positive', 'delicious top grade'], ['service', 'service general', 'positive', 'fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I am happy i did the food was awsome .']", "output": "[['food', 'food quality', 'positive', 'awsome']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We ordered some beef and noodle soup dishes from the Thai section of the menu but nothing we got was Thai .']", "output": "[['beef and noodle soup dishes', 'food quality', 'negative', 'nothing we got was Thai']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A Thai restaurant out of rice during dinner ?']", "output": "[['Thai restaurant', 'restaurant miscellaneous', 'negative', 'out of rice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"When you add it all together , it just does n't seem worth it to me ... especially considering the prices .\"]", "output": "[['NULL', 'restaurant general', 'negative', \"does n't seem worth it\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is the best sushi in new york city - hands down .']", "output": "[['sushi', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When we stumbled on Leon , we thought that we had found quite the gem BUT , we were certainly wrong .']", "output": "[['Leon', 'restaurant general', 'negative', 'certainly wrong']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Mermaid Inn is an overall good restaurant with really good seafood .']", "output": "[['seafood', 'food quality', 'positive', 'good'], ['Mermaid Inn', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['And it was quick which is very important .']", "output": "[['NULL', 'service general', 'positive', 'quick']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was good and food is wonderful .']", "output": "[['Service', 'service general', 'positive', 'good'], ['food', 'food quality', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was excellent and the food was delicious .']", "output": "[['service', 'service general', 'positive', 'excellent'], ['food', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A few tips : skip the turnip cake , roast pork buns and egg custards .']", "output": "[['turnip cake', 'food quality', 'negative', 'skip'], ['roast pork buns', 'food quality', 'negative', 'skip'], ['egg custards', 'food quality', 'negative', 'skip']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The portions are small but being that the food was so good makes up for that .']", "output": "[['portions', 'food style_options', 'negative', 'small'], ['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's a nice place to relax and have conversation .\"]", "output": "[['place', 'ambience general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When we sat , we got great and fast service .']", "output": "[['service', 'service general', 'positive', 'great'], ['service', 'service general', 'positive', 'fast']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['THE BIG COMPLAINT : NO TOASTING AVAILABLE .']", "output": "[['NULL', 'service general', 'negative', 'COMPLAINT']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was exceptional .']", "output": "[['food', 'food quality', 'positive', 'exceptional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I LOVE their Thai']", "output": "[['Thai', 'food quality', 'positive', 'LOVE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['People are always friendly .']", "output": "[['People', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Haru on Park S is simply disgusting .']", "output": "[['Haru on Park S', 'restaurant general', 'negative', 'disgusting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is a great bargain .']", "output": "[['place', 'restaurant prices', 'positive', 'great bargain']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My wife had the fried shrimp which are huge and loved it .']", "output": "[['fried shrimp', 'food style_options', 'positive', 'huge'], ['fried shrimp', 'food quality', 'positive', 'loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Toons has recently been redone , so it 's now a very attractive space .\"]", "output": "[['Toons', 'ambience general', 'positive', 'attractive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Nice Family owned traditional restaurant .']", "output": "[['restaurant', 'restaurant general', 'positive', 'traditional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is a fun restaurant to go to .']", "output": "[['restaurant', 'restaurant general', 'positive', 'fun']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['delicious simple food in nice outdoor atmosphere .']", "output": "[['food', 'food quality', 'positive', 'delicious simple'], ['food', 'food style_options', 'positive', 'delicious simple'], ['outdoor atmosphere', 'ambience general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !']", "output": "[['terrace', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Was surprisingly disappointed .']", "output": "[['NULL', 'food quality', 'negative', 'surprisingly disappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The decor is very simple but comfortable .']", "output": "[['decor', 'ambience general', 'positive', 'simple'], ['decor', 'ambience general', 'positive', 'comfortable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Surprisingly nothing could be further from the truth .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'be further from the truth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Always good drinks and service is pretty good ;']", "output": "[['drinks', 'drinks quality', 'positive', 'good'], ['service', 'service general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\"]", "output": "[['sake list', 'drinks style_options', 'positive', 'extensive'], ['NULL', 'service general', 'positive', 'made for us upon request']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was very disappointed with this restaurant .']", "output": "[['restaurant', 'restaurant general', 'negative', 'disappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We ate at this Thai place following the reviews but very unhappy with the foods .']", "output": "[['foods', 'food quality', 'negative', 'unhappy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We did tip , I guess the model /waitress just wanted more and complained to the manager .']", "output": "[['waitress', 'service general', 'negative', 'complained']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\"]", "output": "[['food', 'food quality', 'positive', 'above average'], ['Teodora', 'restaurant general', 'negative', 'deficiencies']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is definitely a good spot for snacks and chat .']", "output": "[['spot', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I got the $ 10 10-piece dim sum combo , every bite of which was great .']", "output": "[['$ 10 10-piece dim sum combo', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We had a great time at the Jekyll and hyde Pub last night .']", "output": "[['Jekyll and hyde Pub', 'restaurant general', 'positive', 'great time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Keep up the good work guys !']", "output": "[['NULL', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the all-u-can-eat sushi is definitely in very poor quality .']", "output": "[['all-u-can-eat sushi', 'food quality', 'negative', 'poor quality']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great sushi experience .']", "output": "[['sushi', 'food quality', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The duck confit is always amazing and the foie gras terrine with figs was out of this world .']", "output": "[['foie gras terrine with figs', 'food quality', 'positive', 'out of this world'], ['duck confit', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .']", "output": "[['atmosphere', 'ambience general', 'negative', 'noisy'], ['waiters', 'service general', 'positive', 'fast']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The Prix Fixe menu is worth every penny and you get more than enough ( both in quantity AND quality ) .']", "output": "[['Prix Fixe menu', 'food quality', 'positive', 'worth'], ['Prix Fixe menu', 'food style_options', 'positive', 'worth'], ['Prix Fixe menu', 'food prices', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Sauce was watery and the food did n't have much flavor .\"]", "output": "[['Sauce', 'food quality', 'negative', 'watery'], ['food', 'food quality', 'negative', \"did n't have much flavor\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Never have I had such dramatic delivery guys ( a lot of huffing and panting and muttering under breath b/c I live in a walkup ) who always seem disappointed with their tips .']", "output": "[['delivery guys', 'service general', 'negative', 'dramatic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .']", "output": "[['staff', 'service general', 'negative', 'good'], ['Cafe Noir', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I stumbled upon this great pizzeria as I explored my new neighborhood .']", "output": "[['pizzeria', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Prices too high for this cramped and unappealing resturant .']", "output": "[['resturant', 'restaurant prices', 'negative', 'high'], ['resturant', 'ambience general', 'negative', 'cramped'], ['resturant', 'ambience general', 'negative', 'unappealing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everything is always cooked to perfection , the service is excellent , the decor cool and understated .']", "output": "[['NULL', 'food quality', 'positive', 'perfection'], ['service', 'service general', 'positive', 'excellent'], ['decor', 'ambience general', 'positive', 'cool'], ['decor', 'ambience general', 'positive', 'understated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everything about this restaurant was special .']", "output": "[['restaurant', 'restaurant general', 'positive', 'special']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Warm and friendly in the winter and terrific outdoor seating in the warmer months .']", "output": "[['NULL', 'ambience general', 'positive', 'Warm'], ['NULL', 'ambience general', 'positive', 'friendly'], ['outdoor seating', 'ambience general', 'positive', 'terrific']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I have been to Roth 's twice and both times were very disappointing .\"]", "output": "[[\"Roth 's\", 'restaurant general', 'negative', 'disappointing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best Pastrami I ever had and great portion without being ridiculous .']", "output": "[['Pastrami', 'food quality', 'positive', 'Best'], ['portion', 'food style_options', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service here was great , food was fantastic .']", "output": "[['Service', 'service general', 'positive', 'great'], ['food', 'food quality', 'positive', 'fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"If celebrities make you sweat , then your in for a ride , but if your like most around these parts then you 'll just yawn and wonder whats with all the hype .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'yawn']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It may be a bit packed on weekends , but the vibe is good and it is the best French food you will find in the area .']", "output": "[['NULL', 'ambience general', 'neutral', 'packed'], ['vibe', 'ambience general', 'positive', 'good'], ['French food', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['love the food .']", "output": "[['food', 'food quality', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The combination of super-fresh ingredients in the dishes are unusual but really delicious .']", "output": "[['ingredients', 'food quality', 'positive', 'super-fresh'], ['ingredients', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ive asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away .']", "output": "[['cart attendant', 'service general', 'negative', 'walked away']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I pray it stays open forever .']", "output": "[['NULL', 'restaurant general', 'positive', 'stays open forever']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was slow , but the people were friendly .']", "output": "[['Service', 'service general', 'negative', 'slow'], ['people', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Lucky Strike is a great casual place to just grab a bite to eat .']", "output": "[['Lucky Strike', 'restaurant general', 'positive', 'great casual']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\"]", "output": "[['food', 'food quality', 'positive', 'amazing'], ['selection of wines', 'drinks style_options', 'positive', 'extensive'], [\"Chef 's tasting menu\", 'food quality', 'positive', 'favourite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Baluchi 's has solid food and a nice decor at reasonable prices .\"]", "output": "[['food', 'food quality', 'positive', 'solid'], ['decor', 'ambience general', 'positive', 'nice'], [\"Baluchi 's\", 'restaurant prices', 'positive', 'solid']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['As many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .']", "output": "[['NULL', 'service general', 'neutral', 'slow']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .']", "output": "[['service', 'service general', 'positive', 'impeccable'], ['service', 'service general', 'positive', 'unobtrusive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I have been to Rao 's probably 15 times the past 3 years and it keeps getting better .\"]", "output": "[[\"Rao 's\", 'restaurant general', 'positive', 'better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I will be out with friends and all of a sudden I am hungry and I only crave one thing ... their Pizza .']", "output": "[['Pizza', 'food quality', 'positive', 'crave']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .']", "output": "[['atmosphere', 'ambience general', 'positive', 'nothing special']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['IT WAS HORRIBLE .']", "output": "[['NULL', 'restaurant general', 'negative', 'HORRIBLE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But , they were too big for the bun .']", "output": "[['NULL', 'food style_options', 'negative', 'too big']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Thanks Bloom 's for a lovely trip .\"]", "output": "[[\"Bloom 's\", 'restaurant general', 'positive', 'Thanks'], [\"Bloom 's\", 'restaurant general', 'positive', 'lovely']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I love it .']", "output": "[['NULL', 'restaurant general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Spreads and toppings are great - though a bit pricey .']", "output": "[['Spreads', 'food quality', 'positive', 'great'], ['toppings', 'food quality', 'positive', 'great'], ['Spreads', 'food prices', 'negative', 'pricey'], ['toppings', 'food prices', 'negative', 'pricey']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The food 's as good as ever .\"]", "output": "[['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Decor is charming .']", "output": "[['Decor', 'ambience general', 'positive', 'charming']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was good .']", "output": "[['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Delicate spices , onions , eggs and a kick-ass roti .']", "output": "[['spices', 'food quality', 'positive', 'Delicate'], ['onions', 'food quality', 'positive', 'Delicate'], ['eggs', 'food quality', 'positive', 'Delicate'], ['roti', 'food quality', 'positive', 'kick-ass']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The pesto pizza was excellent , thin-crust pizza with a nice amount of spicy Italian cheese that I 'd never heard of before .\"]", "output": "[['pesto pizza', 'food quality', 'positive', 'excellent'], ['spicy Italian cheese', 'food quality', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wonderful at holiday time .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'Wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is great and they have a good selection of wines at reasonable prices .']", "output": "[['food', 'food quality', 'positive', 'great'], ['wines', 'drinks style_options', 'positive', 'good selection'], ['wines', 'drinks prices', 'positive', 'good selection']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .']", "output": "[['meals', 'food quality', 'negative', 'edible'], ['rosemary or orange flavoring', 'food quality', 'negative', 'weird']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would defiantly come back here again as one of my top choices .']", "output": "[['NULL', 'restaurant general', 'positive', 'top choices']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have lived in Japan for 7 years and the taste of the food and the feel of the restaurant is like being back in Japan .']", "output": "[['food', 'food quality', 'positive', 'like being back in Japan'], ['feel', 'ambience general', 'positive', 'like being back in Japan']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I found the food , service and value exceptional everytime I have been there .']", "output": "[['food', 'food quality', 'positive', 'exceptional'], ['service', 'service general', 'positive', 'exceptional'], ['value', 'restaurant prices', 'positive', 'exceptional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The rice was poor quality and was cooked so badly it was hard .']", "output": "[['rice', 'food quality', 'negative', 'poor quality'], ['rice', 'food quality', 'negative', 'cooked so badly'], ['rice', 'food quality', 'negative', 'hard']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food now is inconsistent .']", "output": "[['food', 'food quality', 'negative', 'inconsistent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Cute and decorative .']", "output": "[['NULL', 'ambience general', 'positive', 'Cute'], ['NULL', 'ambience general', 'positive', 'decorative']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is always very crowded and popular .']", "output": "[['place', 'restaurant miscellaneous', 'positive', 'crowded'], ['place', 'restaurant miscellaneous', 'positive', 'popular']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"When you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside .\"]", "output": "[['main dining room', 'ambience general', 'positive', 'spectacular'], ['ceiling', 'ambience general', 'positive', 'spectacular'], ['ceiling', 'ambience general', 'positive', 'hand-painted high']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['sometimes i get bad food and bad service , sometimes i get good good and bad service .']", "output": "[['food', 'food quality', 'negative', 'bad'], ['service', 'service general', 'negative', 'bad'], ['service', 'service general', 'negative', 'bad'], ['good', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['There is a lot of variety even for people who eat vegetarian like me .']", "output": "[['NULL', 'food style_options', 'positive', 'a lot of variety']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['whoever the jazz duo was , they were on POINT .']", "output": "[['jazz duo', 'ambience general', 'positive', 'on POINT']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Most importantly , food is excellent .']", "output": "[['food', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was pleasantly surprised to find this gem in Hoboken .']", "output": "[['NULL', 'restaurant general', 'positive', 'pleasantly surprised']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['noodles with shrimp and chicken and coconut juice is the MUST !']", "output": "[['noodles with shrimp and chicken and coconut juice', 'food quality', 'positive', 'MUST']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['However , I think this place is a good hang out spot .']", "output": "[['place', 'ambience general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !']", "output": "[['spot', 'restaurant miscellaneous', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is expensive but well worth the money .']", "output": "[['NULL', 'restaurant prices', 'negative', 'expensive'], ['NULL', 'restaurant general', 'positive', 'well worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Cozy romantic atomosphere with only around 15 tables at most .']", "output": "[['atomosphere', 'ambience general', 'positive', 'Cozy romantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's a rather cramped and busy restaurant and it closes early .\"]", "output": "[['restaurant', 'restaurant miscellaneous', 'negative', 'closes early'], ['restaurant', 'ambience general', 'negative', 'cramped'], ['restaurant', 'ambience general', 'negative', 'busy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wine list is extensive without being over-priced .']", "output": "[['Wine list', 'drinks style_options', 'positive', 'extensive without being over-priced'], ['Wine list', 'drinks prices', 'positive', 'extensive without being over-priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"And even with it 's Pub atmosphere they were great to my kids too !\"]", "output": "[['NULL', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have been doing all of the above at the Heartland Brewery for over 5 years now and I HAVE NEVER BEEN DISAPPOINTED !']", "output": "[['Heartland Brewery', 'restaurant general', 'positive', 'NEVER BEEN DISAPPOINTED']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Right off the L in Brooklyn this is a nice cozy place with good pizza .']", "output": "[['pizza', 'food quality', 'positive', 'good'], ['place', 'ambience general', 'positive', 'nice cozy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food here does a great service to the name ( Cantonese that is ... ) .']", "output": "[['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Myagi is one of my favorite restaurants in the City ; the place the negative reviews describe sound like they were somewhere else .']", "output": "[['Myagi', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Wouldn 't recomend it for dinner !\"]", "output": "[['NULL', 'restaurant general', 'negative', \"Wouldn 't recomend\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I ordered the smoked salmon and roe appetizer and it was off flavor .']", "output": "[['smoked salmon and roe appetizer', 'food quality', 'negative', 'off flavor']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was average or above including some surprising tasty dishes .']", "output": "[['food', 'food quality', 'positive', 'average or above'], ['dishes', 'food quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['And really large portions .']", "output": "[['portions', 'food style_options', 'positive', 'large']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service , however , was excellent ... and I liked the setting/atmosphere a lot .']", "output": "[['Service', 'service general', 'positive', 'excellent'], ['setting/atmosphere', 'ambience general', 'positive', 'liked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend it .']", "output": "[['NULL', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['fine dining restaurant quality .']", "output": "[['dining', 'food quality', 'positive', 'fine']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Nice atmosphere , the service was very pleasant and the desert was good .']", "output": "[['atmosphere', 'ambience general', 'positive', 'Nice'], ['service', 'service general', 'positive', 'pleasant'], ['desert', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['LOVE the atmosphere - felt like I was in Paris .']", "output": "[['atmosphere', 'ambience general', 'positive', 'LOVE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would highly recommand requesting a table by the window .']", "output": "[['table by the window', 'location general', 'positive', 'recommand']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .']", "output": "[['sushi', 'food quality', 'positive', 'great'], ['thai food', 'food quality', 'positive', 'excellent'], ['Planet Thailand', 'restaurant general', 'positive', 'hit']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The flavors robust and subtle .']", "output": "[['NULL', 'food quality', 'positive', 'robust'], ['NULL', 'food quality', 'positive', 'subtle']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Still , any quibbles about the bill were off-set by the pour-your-own measures of liquers which were courtesey of the house ...']", "output": "[['NULL', 'restaurant prices', 'neutral', 'quibbles'], ['measures of liquers', 'drinks style_options', 'positive', 'pour-your-own'], ['measures of liquers', 'drinks style_options', 'positive', 'courtesey']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was mediocre at best but it was the horrible service that made me vow never to go back .']", "output": "[['food', 'food quality', 'negative', 'mediocre'], ['service', 'service general', 'negative', 'horrible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Such a disappointment ...']", "output": "[['NULL', 'service general', 'negative', 'disappointment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Our food was great too !']", "output": "[['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Overall , not worth the money .']", "output": "[['NULL', 'restaurant prices', 'negative', 'not worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is pricey , and yes , the food is worth it ; but the service makes you feel like you should be paying a quater of the price .']", "output": "[['place', 'restaurant prices', 'negative', 'pricey'], ['food', 'food quality', 'positive', 'worth'], ['service', 'service general', 'negative', 'paying a quater of the price']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The mussels were fantastic and so was the dessert ... definitely going to be back very soon .']", "output": "[['mussels', 'food quality', 'positive', 'fantastic'], ['dessert', 'food quality', 'positive', 'fantastic'], ['NULL', 'restaurant general', 'positive', 'going to be back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"if you 're daring , try the balsamic vinegar over icecream , it 's wonderful !\"]", "output": "[['balsamic vinegar over icecream', 'food quality', 'positive', 'try'], ['balsamic vinegar over icecream', 'food quality', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Overall , excellent restaurant !']", "output": "[['restaurant', 'restaurant general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My wife and I ate here earlier this week and have not stopped ranting and raving about the food .']", "output": "[['food', 'food quality', 'positive', 'raving']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A little crowded but they move that line really fast !']", "output": "[['NULL', 'service general', 'positive', 'fast'], ['NULL', 'restaurant miscellaneous', 'negative', 'crowded']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Go there once and oh yes ... you will go back ... you will ...']", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['it is not consistent .']", "output": "[['NULL', 'restaurant general', 'negative', 'not consistent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The pizza was delivered cold and the cheese was n't even fully melted !\"]", "output": "[['pizza', 'food quality', 'negative', 'cold'], ['cheese', 'food quality', 'negative', \"was n't even fully melted\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .']", "output": "[['staff', 'service general', 'positive', 'nice'], ['staff', 'service general', 'negative', 'stressed'], ['unisex bathroom', 'ambience general', 'negative', 'needs to be cleaned']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Unique apppetizers .']", "output": "[['apppetizers', 'food quality', 'positive', 'Unique']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .']", "output": "[['restaurant', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The service is ok , some of the people did n't get what they asked for .\"]", "output": "[['service', 'service general', 'neutral', 'ok']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Simply some good tasting Chinese food at incredible prices ...']", "output": "[['Chinese food', 'food quality', 'positive', 'good tasting'], ['Chinese food', 'food prices', 'positive', 'good tasting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've been to at Cafe Spice probably 5-8 times , it is probably still the best Indian restaurant around Union Square .\"]", "output": "[['Cafe Spice', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['La Rosa waltzes in , and I think they are doing it the best .']", "output": "[['La Rosa', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The design and atmosphere is just as good .']", "output": "[['design', 'ambience general', 'positive', 'good'], ['atmosphere', 'ambience general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\"]", "output": "[['host', 'service general', 'positive', 'amazing'], ['crew', 'service general', 'positive', 'treated me as family']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But the pizza is way to expensive .']", "output": "[['pizza', 'food prices', 'negative', 'expensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['While their kitchen food is delicious , their Sushi is out of this world .']", "output": "[['kitchen food', 'food quality', 'positive', 'delicious'], ['Sushi', 'food quality', 'positive', 'out of this world']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Indoor was very cozy and cute .']", "output": "[['Indoor', 'ambience general', 'positive', 'cozy'], ['Indoor', 'ambience general', 'positive', 'cute']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .']", "output": "[['brioche and lollies', 'food quality', 'positive', 'cute'], ['brioche and lollies', 'food quality', 'positive', 'sweet']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is authentic Italian - delicious !']", "output": "[['food', 'food quality', 'positive', 'authentic Italian'], ['food', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is some really good , inexpensive sushi .']", "output": "[['sushi', 'food quality', 'positive', 'good'], ['sushi', 'food prices', 'positive', 'inexpensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We went around 9:30 on a Friday and it had died down a bit by then so the service was great !']", "output": "[['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try everything for that matter , it is all good .']", "output": "[['NULL', 'food quality', 'positive', 'all good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was the only thing good about this restaurant .']", "output": "[['service', 'service general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .']", "output": "[['Chow fun', 'food quality', 'negative', 'dry'], ['pork shu mai', 'food quality', 'negative', 'greasy'], ['NULL', 'ambience general', 'negative', 'loud'], ['NULL', 'ambience general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had a grat time at Jekyll and Hyde !']", "output": "[['Jekyll and Hyde', 'restaurant general', 'positive', 'had a grat time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I really recommend the very simple Unda ( Egg ) rolls .']", "output": "[['Unda ( Egg ) rolls', 'food quality', 'positive', 'recommend'], ['Unda ( Egg ) rolls', 'food quality', 'positive', 'simple']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Try the Pad Thai , or sample anything on the appetizer menu ... they 're all delicious .\"]", "output": "[['Pad Thai', 'food quality', 'positive', 'Try'], ['Pad Thai', 'food quality', 'positive', 'delicious'], ['appetizer menu', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The view is spectacular , and the food is great .']", "output": "[['view', 'location general', 'positive', 'spectacular'], ['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's very spicy but not offensive .\"]", "output": "[['NULL', 'food quality', 'positive', 'not offensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['First went here to enjoy their garden terrace .']", "output": "[['garden terrace', 'ambience general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best Taiwanese food in NY !']", "output": "[['Taiwanese food', 'food quality', 'positive', 'Best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The scallion pancakes and fried dumplings were nothing out of the ordinary .']", "output": "[['scallion pancakes', 'food quality', 'neutral', 'ordinary'], ['fried dumplings', 'food quality', 'neutral', 'ordinary']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Their tuna tartar appetizer is to die for .']", "output": "[['tuna tartar appetizer', 'food quality', 'positive', 'die for']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .']", "output": "[['NULL', 'restaurant general', 'positive', 'DO NOT pass it up']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We liked it so much , that we will always make it a point to dine here when we visit New York .']", "output": "[['NULL', 'restaurant general', 'positive', 'liked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['However , it is jus too good to not praise it .']", "output": "[['NULL', 'restaurant general', 'positive', 'good'], ['NULL', 'restaurant general', 'positive', 'praise']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place blew me away ... by far my new favorite restaurant on the uppereast side .']", "output": "[['place', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend to anyone to give this place a try .']", "output": "[['place', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['There was a small wait , but shorter than I expected .']", "output": "[['wait', 'service general', 'positive', 'small'], ['wait', 'service general', 'positive', 'shorter']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the salads are delicious , both refreshing and very spicy .']", "output": "[['salads', 'food quality', 'positive', 'delicious'], ['salads', 'food quality', 'positive', 'refreshing'], ['salads', 'food quality', 'positive', 'spicy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Also very inexpensive .']", "output": "[['NULL', 'restaurant prices', 'positive', 'inexpensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\"]", "output": "[['food', 'food prices', 'negative', 'overpriced'], ['wait-staff', 'service general', 'negative', 'arrogant'], ['service', 'service general', 'negative', 'clumsy'], ['management', 'service general', 'negative', \"does n't care\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only thing more wonderful than the food ( which is exceptional ) is the service .']", "output": "[['food', 'food quality', 'positive', 'exceptional'], ['service', 'service general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Do n't be fooled by crowds of people .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'fooled']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['not sure why this restaurant would be rated that highly .']", "output": "[['restaurant', 'restaurant general', 'negative', 'highly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Bagels are ok , but be sure not to make any special requests !']", "output": "[['Bagels', 'food quality', 'neutral', 'ok'], ['NULL', 'service general', 'negative', 'not to make any special requests']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I will be going back and heartily recommend it !']", "output": "[['NULL', 'restaurant general', 'positive', 'going back'], ['NULL', 'restaurant general', 'positive', 'heartily recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good drink .']", "output": "[['drink', 'drinks quality', 'positive', 'Good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great Indian food and the service is incredible .']", "output": "[['Indian food', 'food quality', 'positive', 'Great'], ['service', 'service general', 'positive', 'incredible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Located at the end of a magnificent block .']", "output": "[['NULL', 'location general', 'positive', 'magnificent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We have gone for dinner only a few times but the same great quality and service is given .']", "output": "[['service', 'service general', 'positive', 'great'], ['dinner', 'food quality', 'positive', 'great quality']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Its dark , and cozy . . there is always jazz music playing when we go .']", "output": "[['NULL', 'ambience general', 'positive', 'cozy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place has great indian chinese food .']", "output": "[['indian chinese food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not what I would expect for the price and prestige of this location .']", "output": "[['location', 'restaurant prices', 'neutral', 'expect'], ['location', 'restaurant miscellaneous', 'neutral', 'expect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Finally a reliable Chinese restaurant !']", "output": "[['Chinese restaurant', 'restaurant general', 'positive', 'reliable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lobster knuckles ( special of the day ) were ok , but pretty tasteless .']", "output": "[['lobster knuckles', 'food style_options', 'neutral', 'ok'], ['lobster knuckles', 'food quality', 'negative', 'tasteless']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The menu is fairly simple without much descriptions .']", "output": "[['menu', 'food style_options', 'neutral', 'simple']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['WORST PLACE ON SMITH STREET IN BROOKLYN']", "output": "[['PLACE', 'restaurant general', 'negative', 'WORST']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .']", "output": "[['staff', 'service general', 'positive', 'nice'], ['staff', 'service general', 'negative', 'stressed'], ['unisex bathroom', 'ambience general', 'negative', 'needs to be cleaned']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I absolutely love this place ! ! !']", "output": "[['place', 'restaurant general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Never got an explanation as to what was going on .']", "output": "[['NULL', 'service general', 'negative', 'Never got an explanation']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everyone seemed generally happy with their food , except my brother who had the grilled Mahi Mahi , seemingly drenched in Grapfruit Juice !']", "output": "[['food', 'food quality', 'positive', 'happy'], ['grilled Mahi Mahi', 'food quality', 'negative', 'drenched'], ['grilled Mahi Mahi', 'food style_options', 'negative', 'drenched']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Excellent atmosphere , delicious dishes good and friendly service .']", "output": "[['atmosphere', 'ambience general', 'positive', 'Excellent'], ['dishes', 'food quality', 'positive', 'delicious'], ['service', 'service general', 'positive', 'good'], ['service', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Consequently , their burgers fell apart in their hands and made such a mess that they did'nt feel like finishing them .\"]", "output": "[['burgers', 'food style_options', 'negative', 'fell apart']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'm partial to the Gnocchi .\"]", "output": "[['Gnocchi', 'restaurant general', 'positive', 'partial']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waiter was attentive .']", "output": "[['waiter', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My chicken was inedible as there were so many fatty lumps which i had to keep spitting out into my napkin .']", "output": "[['chicken', 'food quality', 'negative', 'inedible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I completely recommend Casa La Femme for any special occasion and to REALLY impress your date .']", "output": "[['Casa La Femme', 'restaurant miscellaneous', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Even though its good seafood , the prices are too high .']", "output": "[['seafood', 'food quality', 'positive', 'good'], ['NULL', 'restaurant prices', 'negative', 'high']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I would say that all was fine and tasty but the heaviness on my stomach someting that i ca n't not mention or undermine .\"]", "output": "[['NULL', 'food quality', 'negative', \"the heaviness on my stomach someting that i ca n't not mention or undermine\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service not the friendliest to our `` large party `` !']", "output": "[['Service', 'service general', 'negative', 'not the friendliest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['While the ambiance and atmosphere were great , the food and service could have been a lot better .']", "output": "[['ambiance', 'ambience general', 'positive', 'great'], ['atmosphere', 'ambience general', 'positive', 'great'], ['food', 'food quality', 'negative', 'could have been a lot better'], ['service', 'service general', 'negative', 'could have been a lot better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Looking around , I saw a room full of New Yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .']", "output": "[['meal', 'food quality', 'positive', 'real'], ['restaurant', 'ambience general', 'positive', 'real']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Can 't wait to go back .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Sometimes tables do n't understand his sense of humor but it 's refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\"]", "output": "[['server', 'service general', 'positive', 'refreshing'], ['server', 'service general', 'positive', 'professionalism'], ['server', 'service general', 'positive', 'respects']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would highly recommand requesting a table by the window .']", "output": "[['table by the window', 'location general', 'positive', 'recommand']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Mine tasted like the bartender had forgotten to add the tequila .']", "output": "[['NULL', 'drinks quality', 'negative', 'forgotten']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Helpful service and average price per dish $ 10 .']", "output": "[['service', 'service general', 'positive', 'Helpful'], ['dish', 'food prices', 'neutral', '$ 10']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"This is the kind of place you 'd like to take all your friends to and still keep a secret .\"]", "output": "[['place', 'restaurant miscellaneous', 'positive', 'like']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've had my fair share of modern Japanese and this spot delivers .\"]", "output": "[['modern Japanese', 'food quality', 'positive', 'delivers']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was slow had to wait to order and get food although not crowded .']", "output": "[['Service', 'service general', 'negative', 'slow']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\"]", "output": "[['service', 'service general', 'positive', 'excellent'], ['place', 'ambience general', 'negative', 'crowded'], ['place', 'restaurant miscellaneous', 'positive', 'small'], ['place', 'restaurant miscellaneous', 'positive', 'intimate']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best Pastrami I ever had and great portion without being ridiculous .']", "output": "[['Pastrami', 'food quality', 'positive', 'Best'], ['portion', 'food style_options', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Still we keep going back : )']", "output": "[['NULL', 'restaurant general', 'positive', 'going back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['SO GOOD']", "output": "[['NULL', 'restaurant general', 'positive', 'GOOD']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Highly recommended to all !']", "output": "[['NULL', 'restaurant general', 'positive', 'recommended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Creative , consistent , fresh .']", "output": "[['NULL', 'food quality', 'positive', 'consistent'], ['NULL', 'food style_options', 'positive', 'Creative'], ['NULL', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Eating in , the atmosphere saves it , but at your desk , it 's a very disappointing experience .\"]", "output": "[['atmosphere', 'ambience general', 'positive', 'saves'], ['NULL', 'restaurant general', 'negative', 'disappointing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Love the scene first off- the place has a character and nice light to it..very fortunate , location wise .']", "output": "[['scene', 'ambience general', 'positive', 'Love'], ['place', 'ambience general', 'positive', 'nice'], ['location', 'location general', 'positive', 'fortunate']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The real problem I had with this place was the complete lack of service .']", "output": "[['service', 'service general', 'negative', 'lack of']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['How do you rate home ?']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'home']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Patroon features a nice cigar bar and has great staff .']", "output": "[['cigar bar', 'ambience general', 'positive', 'nice'], ['staff', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was prompt and courteous .']", "output": "[['Service', 'service general', 'positive', 'prompt'], ['Service', 'service general', 'positive', 'courteous']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .']", "output": "[['beers', 'drinks style_options', 'positive', 'happy'], ['food', 'food quality', 'positive', 'delicious'], ['pumkin tortelini', 'food quality', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The family seafood entree was very good .']", "output": "[['family seafood entree', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['No gimmicks here -- the food speaks for itself in its freshness and preparation .']", "output": "[['food', 'food quality', 'positive', 'freshness']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is very overpriced and not very tasty .']", "output": "[['NULL', 'food quality', 'negative', 'not very tasty'], ['NULL', 'food prices', 'negative', 'overpriced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My wife and I always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .']", "output": "[['staff', 'service general', 'positive', 'enjoy'], ['staff', 'service general', 'positive', 'young'], ['staff', 'service general', 'positive', 'not always well trained'], ['staff', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is so romantic .']", "output": "[['NULL', 'ambience general', 'positive', 'romantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Our food was great too !']", "output": "[['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Have eaten at Ginger House several times , and it 's always good .\"]", "output": "[['Ginger House', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I wanted to go there to see if it was worth it and sadly , curiousity got the best of me and I paid dearly for it .']", "output": "[['NULL', 'restaurant general', 'negative', 'sadly'], ['NULL', 'restaurant prices', 'negative', 'paid dearly for it']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We will be back .']", "output": "[['NULL', 'restaurant general', 'positive', 'be back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The veal was incredible last night .']", "output": "[['veal', 'food quality', 'positive', 'incredible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But that is highly forgivable .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'highly forgivable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We love the food , drinks , and atmosphere !']", "output": "[['food', 'food quality', 'positive', 'love'], ['drinks', 'drinks quality', 'positive', 'love'], ['atmosphere', 'ambience general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Service was just ok , it is not what you 'd expect for $ 500 .\"]", "output": "[['Service', 'service general', 'negative', 'ok'], ['NULL', 'restaurant prices', 'negative', '$ 500']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"if you 're daring , try the balsamic vinegar over icecream , it 's wonderful !\"]", "output": "[['balsamic vinegar over icecream', 'food quality', 'positive', 'try'], ['balsamic vinegar over icecream', 'food quality', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My girlfriend , being slightly more aggressive , and having been equally disgusted causing her to throw out the remainder of her barely eaten meal , called back only to be informed that I was probably wrong and that it was most likely an oyster , and that we were also blacklisted from their restaurant .']", "output": "[['meal', 'food quality', 'negative', 'disgusted'], ['NULL', 'service general', 'negative', 'blacklisted']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine list is interesting and has many good values .']", "output": "[['wine list', 'drinks style_options', 'positive', 'interesting'], ['wine list', 'drinks prices', 'positive', 'good values']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Servers are all different , Greg is my favorite .']", "output": "[['Greg', 'service general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['great taste']", "output": "[['NULL', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place is so cool and the service is prompt and curtious .']", "output": "[['service', 'service general', 'positive', 'prompt'], ['service', 'service general', 'positive', 'curtious'], ['place', 'ambience general', 'positive', 'cool']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I did n't complain , I liked the atmosphere so much .\"]", "output": "[['atmosphere', 'ambience general', 'positive', 'liked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The entree was also very good .']", "output": "[['entree', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Can \u2019 t believe how an expensive NYC restaurant can be so disrespectful to its clients .']", "output": "[['restaurant', 'restaurant prices', 'negative', 'expensive'], ['NULL', 'service general', 'negative', 'disrespectful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great survice']", "output": "[['survice', 'service general', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good food .']", "output": "[['food', 'food quality', 'positive', 'Good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Much of the time it seems like they do not care about you .']", "output": "[['NULL', 'service general', 'negative', 'do not care about you']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food was OK .']", "output": "[['Food', 'food quality', 'neutral', 'OK']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['So close , but not good enough .']", "output": "[['NULL', 'restaurant general', 'neutral', 'not good enough']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Went here on sat 1 /26 and was disappointed .']", "output": "[['NULL', 'restaurant general', 'negative', 'disappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the food is decent .']", "output": "[['food', 'food quality', 'neutral', 'decent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['For authentic Thai food , look no further than Toons .']", "output": "[['Thai food', 'food quality', 'positive', 'authentic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wife had the risotto which was amazing .']", "output": "[['risotto', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great , original taste .']", "output": "[['NULL', 'food quality', 'positive', 'Great'], ['NULL', 'food quality', 'positive', 'original']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They smell like they stuff them with old canned vegetables like the spinach mushroom calzone .']", "output": "[['NULL', 'food quality', 'negative', 'old canned vegetables']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Overall , not worth the money .']", "output": "[['NULL', 'restaurant prices', 'negative', 'not worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The bread was stale , the salad was overpriced and empty .']", "output": "[['bread', 'food quality', 'negative', 'stale'], ['salad', 'food prices', 'negative', 'overpriced'], ['salad', 'food style_options', 'negative', 'empty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['While their kitchen food is delicious , their Sushi is out of this world .']", "output": "[['kitchen food', 'food quality', 'positive', 'delicious'], ['Sushi', 'food quality', 'positive', 'out of this world']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['He offers subpar service and has no personality .']", "output": "[['service', 'service general', 'negative', 'subpar']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I go out to eat and like my courses , servers are patient and never rush courses or force another drink .']", "output": "[['servers', 'service general', 'positive', 'patient']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We had the scallops as an appetizer and they were delicious and the sauce was wonderful .']", "output": "[['scallops', 'food quality', 'positive', 'delicious'], ['sauce', 'food quality', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['not sure why this restaurant would be rated that highly .']", "output": "[['restaurant', 'restaurant general', 'negative', 'highly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['AMAZING MY FAVORITE ! ! !']", "output": "[['NULL', 'restaurant general', 'positive', 'FAVORITE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is my first time writing a review for a restaurant because the food and service was excellent .']", "output": "[['food', 'food quality', 'positive', 'excellent'], ['service', 'service general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great sake !']", "output": "[['sake', 'drinks quality', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The dessert ( we had a pear torte ) was good - but , once again , the staff was unable to provide appropriate drink suggestions .']", "output": "[['pear torte', 'food quality', 'positive', 'good'], ['staff', 'service general', 'negative', 'unable to provide']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You would think they would make up for it with service , sadly , no .']", "output": "[['service', 'service general', 'negative', 'sadly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had a huge pastrami sandwich on a roll .']", "output": "[['pastrami sandwich on a roll', 'food style_options', 'neutral', 'huge']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The Dancing , White River and Millenium rolls are musts .']", "output": "[['Dancing , White River and Millenium rolls', 'food quality', 'positive', 'musts']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is pricey , and yes , the food is worth it ; but the service makes you feel like you should be paying a quater of the price .']", "output": "[['place', 'restaurant prices', 'negative', 'pricey'], ['food', 'food quality', 'positive', 'worth'], ['service', 'service general', 'negative', 'paying a quater of the price']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Never have I had such dramatic delivery guys ( a lot of huffing and panting and muttering under breath b/c I live in a walkup ) who always seem disappointed with their tips .']", "output": "[['delivery guys', 'service general', 'negative', 'dramatic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try the sea bass .']", "output": "[['sea bass', 'food quality', 'positive', 'Try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Guess what , I waited for TWENTY minutes before she came over and when she finally did , she says , `` oh well , I wish you would have said something earlier `` No apology , nothing .']", "output": "[['NULL', 'service general', 'negative', 'No apology']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is a must visit !']", "output": "[['place', 'restaurant general', 'positive', 'must visit']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I take all my NYC guests to VT 's .\"]", "output": "[[\"VT 's\", 'restaurant miscellaneous', 'positive', 'take all my NYC guests']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It is set far from the small street it 's on , and there is no traffic noise .\"]", "output": "[['NULL', 'location general', 'positive', 'no traffic noise']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Fish was overdone .']", "output": "[['Fish', 'food quality', 'negative', 'overdone']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I also recommend the rice dishes or the different varieties of congee ( rice porridge ) .']", "output": "[['rice dishes', 'food quality', 'positive', 'recommend'], ['congee ( rice porridge )', 'food quality', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's a small cute restaurant .\"]", "output": "[['restaurant', 'restaurant general', 'positive', 'small cute']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This tiny Williamsburg spot is always pleasantly surprising .']", "output": "[['Williamsburg spot', 'restaurant general', 'positive', 'surprising']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .']", "output": "[['staff', 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's simply the best meal in NYC .\"]", "output": "[['meal', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would never recommend this place to anybody even for a casual dinner .']", "output": "[['place', 'restaurant general', 'negative', 'never recommend'], ['place', 'restaurant miscellaneous', 'negative', 'never recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff is very good .']", "output": "[['staff', 'service general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The cream cheeses are out of this world and I love that coffee ! !']", "output": "[['cream cheeses', 'food quality', 'positive', 'out of this world'], ['coffee', 'drinks quality', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food awesome .']", "output": "[['Food', 'food quality', 'positive', 'awesome']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar-soaked rice , and the scallion on top of the fish .']", "output": "[['soy sauce', 'food quality', 'negative', 'salty'], ['rice', 'food quality', 'negative', 'vinegar-soaked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good drink .']", "output": "[['drink', 'drinks quality', 'positive', 'Good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service leaves much to be desired , from feeling like you are rushed the place your order , to being ignored the rest of the night .']", "output": "[['service', 'service general', 'negative', 'leaves much to be desired']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is also extremely well priced .']", "output": "[['NULL', 'food prices', 'positive', 'well priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They were such a rip-off ( $ 8 .95 for four small meat patties in steamed buns ) and not worth trying .']", "output": "[['NULL', 'food quality', 'negative', 'rip-off'], ['NULL', 'food style_options', 'negative', 'small'], ['NULL', 'food prices', 'negative', 'not worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .']", "output": "[['crust', 'food quality', 'positive', 'great'], ['crust', 'food quality', 'positive', 'good'], ['sauce', 'food quality', 'positive', 'light'], ['cheese', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"but when we looked at the menu , there weren 't a lot of choices , most of them were dumplings in the appetizer section .\"]", "output": "[['menu', 'food style_options', 'negative', \"weren 't a lot of choices\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The price is reasonable although the service is poor .']", "output": "[['NULL', 'restaurant prices', 'positive', 'reasonable'], ['service', 'service general', 'negative', 'poor']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Remind me of home .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'home']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"We paid and left because we didn 't feel like arguing any more .\"]", "output": "[['NULL', 'service general', 'negative', 'arguing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We all agreed that mare is one of the best seafood restaurants in New York .']", "output": "[['mare', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A classic !']", "output": "[['NULL', 'food quality', 'positive', 'classic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria 's .\"]", "output": "[['pizza', 'food quality', 'positive', 'delicious'], ['fresh mozzarella', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food .']", "output": "[['food', 'food quality', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I generally like this place .']", "output": "[['place', 'restaurant general', 'positive', 'like']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['However , our main course was wonderful .']", "output": "[['main course', 'food quality', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were drawn into the belly dancing show that captivated the crowd .']", "output": "[['belly dancing show', 'ambience general', 'positive', 'captivated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have to highly recommend the lobster roll - not to much mayo ; you can tell it was a fresh lobster .']", "output": "[['lobster roll', 'food quality', 'positive', 'recommend'], ['lobster', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service is good and the resturant is clean .']", "output": "[['service', 'service general', 'positive', 'good'], ['resturant', 'ambience general', 'positive', 'clean']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I really recommend the very simple Unda ( Egg ) rolls .']", "output": "[['Unda ( Egg ) rolls', 'food quality', 'positive', 'recommend'], ['Unda ( Egg ) rolls', 'food quality', 'positive', 'simple']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The environment is very upscale and you will see a lot of rich guys with trophy wives or just highly paid escorts .']", "output": "[['environment', 'ambience general', 'neutral', 'upscale']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everything on the menu is great .']", "output": "[['menu', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['everyone was cheerfully cooperative and helpful .']", "output": "[['NULL', 'service general', 'positive', 'cheerfully cooperative'], ['NULL', 'service general', 'positive', 'helpful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\"]", "output": "[['wait', 'service general', 'negative', 'long'], ['atmosphere', 'ambience general', 'negative', 'raucous'], ['NULL', 'restaurant miscellaneous', 'negative', 'sleek']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place blew me away ... by far my new favorite restaurant on the uppereast side .']", "output": "[['place', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .']", "output": "[['Food', 'food quality', 'positive', 'good'], ['pork belly', 'food quality', 'negative', 'roasted a bit longer']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I heard the lobster roll was excellent .']", "output": "[['lobster roll', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's a rather cramped and busy restaurant and it closes early .\"]", "output": "[['restaurant', 'restaurant miscellaneous', 'negative', 'closes early'], ['restaurant', 'ambience general', 'negative', 'cramped'], ['restaurant', 'ambience general', 'negative', 'busy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The atmosphere was great .']", "output": "[['atmosphere', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['All the people that I bring there go back on their own and bring THEIR friends !']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I pray it stays open forever .']", "output": "[['NULL', 'restaurant general', 'positive', 'stays open forever']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she 's-way-cuter-than-me-that-b @ # $ * way ) .\"]", "output": "[['wait staff', 'service general', 'positive', 'pleasant'], ['wait staff', 'service general', 'positive', 'fun'], ['wait staff', 'service general', 'positive', 'gorgeous']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['One of the best hot dogs I have ever eaten .']", "output": "[['NULL', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['fine dining restaurant quality .']", "output": "[['dining', 'food quality', 'positive', 'fine']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It melted in my little mouth and the perfect consistency-not too fishy , creamy , and slightly buttery .']", "output": "[['NULL', 'food quality', 'positive', 'perfect consistency'], ['NULL', 'food quality', 'positive', 'not too fishy'], ['NULL', 'food quality', 'positive', 'creamy'], ['NULL', 'food quality', 'positive', 'buttery']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They charge different prices all the time .']", "output": "[['NULL', 'service general', 'negative', 'charge different prices']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I read reviews that called the restaurant too expensive and I thought to myself , but may be it is worth it .']", "output": "[['restaurant', 'restaurant prices', 'negative', 'expensive'], ['restaurant', 'restaurant general', 'negative', 'expensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This tiny restaurant is as cozy as it gets , with that certain Parisian flair .']", "output": "[['restaurant', 'ambience general', 'positive', 'cozy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['and yes Dal Bukhara is so dam good and so are all the kababs .']", "output": "[['kababs', 'food quality', 'positive', 'good'], ['Dal Bukhara', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the crust was imazingly cooked well and pizza was fully loaded : ) : ) : )']", "output": "[['crust', 'food quality', 'positive', 'cooked well'], ['pizza', 'food style_options', 'positive', 'fully loaded']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend the restaurant based on our experience last night .']", "output": "[['restaurant', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were 4 and got the family size penne a la vodka which was tremendously gigantic portion ... a bucket of food literally .']", "output": "[['penne a la vodka', 'food style_options', 'positive', 'gigantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Our server was very helpful and friendly .']", "output": "[['server', 'service general', 'positive', 'helpful'], ['server', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Make sure you have the Spicy Scallop roll ...']", "output": "[['Spicy Scallop roll', 'food quality', 'positive', 'Make sure']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was attentive .']", "output": "[['service', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .']", "output": "[['service', 'service general', 'positive', 'fast'], ['service', 'service general', 'positive', 'friendly'], ['food', 'food quality', 'positive', 'tasty'], ['hot sauce', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I must say I am surprised by the bad reviews of the restaurant earlier in the year , though .']", "output": "[['restaurant', 'restaurant general', 'positive', 'bad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Mistakes happen , but they are usually accompanied by an apology , perhaps even a glass of wine ... but not the grunt that we received from the Al Di La staff .']", "output": "[['staff', 'service general', 'negative', 'grunt']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place is small and cramped but the food is fantastic .']", "output": "[['place', 'ambience general', 'negative', 'small'], ['place', 'ambience general', 'negative', 'cramped'], ['food', 'food quality', 'positive', 'fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I loved this place ! !']", "output": "[['place', 'restaurant general', 'positive', 'loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lunch buffet is expensive but is deff worth it .']", "output": "[['lunch buffet', 'food prices', 'negative', 'expensive'], ['lunch buffet', 'food quality', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .']", "output": "[['outdoor atmosphere', 'location general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service is good and ambience is good for a date or group outing .']", "output": "[['service', 'service general', 'positive', 'good'], ['ambience', 'ambience general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food now is inconsistent .']", "output": "[['food', 'food quality', 'negative', 'inconsistent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['All in all the food was good - a little on the expensive side , but fresh .']", "output": "[['food', 'food quality', 'positive', 'fresh'], ['food', 'food quality', 'positive', 'good'], ['food', 'food prices', 'negative', 'expensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"At $ 120 for two people , however , this in no way represents value , unless you 're looking to pay by the hour for passive-aggressive torture .\"]", "output": "[['NULL', 'restaurant prices', 'negative', 'no way represents value'], ['NULL', 'service general', 'negative', 'torture']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The servers at Flatbush Farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .']", "output": "[['servers', 'service general', 'negative', 'perfected']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is one the nicest outdoor restaurants I have ever seen in NY -- I am from Italy and this place rivals the ones in my country .']", "output": "[['outdoor restaurants', 'ambience general', 'positive', 'nicest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Prices too high for this cramped and unappealing resturant .']", "output": "[['resturant', 'restaurant prices', 'negative', 'high'], ['resturant', 'ambience general', 'negative', 'cramped'], ['resturant', 'ambience general', 'negative', 'unappealing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A quintessential slice of NYC pizza .']", "output": "[['slice of NYC pizza', 'food quality', 'neutral', 'quintessential']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But the staff was so horrible to us .']", "output": "[['staff', 'service general', 'negative', 'horrible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I picked the Grilled Black Cod as my entree , which I absolutely devoured while someone commented that the Grilled Salmon dish was better .']", "output": "[['Grilled Black Cod', 'food quality', 'positive', 'devoured'], ['Grilled Salmon dish', 'food quality', 'positive', 'better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['LOVE the atmosphere - felt like I was in Paris .']", "output": "[['atmosphere', 'ambience general', 'positive', 'LOVE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"One of us actually liked the expresso - that 's it .\"]", "output": "[['expresso', 'drinks quality', 'positive', 'liked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But , they were too big for the bun .']", "output": "[['NULL', 'food style_options', 'negative', 'too big']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The pizza is delicious and the proprietor is one of the nicest in NYC .']", "output": "[['pizza', 'food quality', 'positive', 'delicious'], ['proprietor', 'service general', 'positive', 'nicest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['In the evening , this place attracted a well dressed , with it , NY crowd .']", "output": "[['crowd', 'restaurant miscellaneous', 'positive', 'attracted']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Highly recommended .']", "output": "[['NULL', 'restaurant general', 'positive', 'Highly recommended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"In any event , this is a place I 'll be sure to stop by again when I 'm in this part of town .\"]", "output": "[['place', 'restaurant general', 'positive', 'stop by again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['For me dishes a little oily , but overall dining experience good .']", "output": "[['dishes', 'food quality', 'negative', 'oily'], ['NULL', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wait staff is blantently unappreciative of your business but its the best pie on the UWS !']", "output": "[['Wait staff', 'service general', 'negative', 'unappreciative'], ['pie', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"You ca n't go wrong here .\"]", "output": "[['NULL', 'restaurant general', 'positive', \"ca n't go wrong\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I thought the restaurant was nice and clean .']", "output": "[['restaurant', 'restaurant general', 'positive', 'nice'], ['restaurant', 'ambience general', 'positive', 'clean']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I love this place']", "output": "[['place', 'restaurant general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We will go back every time we are in the City .']", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Mizu is home to creative and unique rolls not to found anywhere else .']", "output": "[['rolls', 'food style_options', 'positive', 'unique']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['To top it all off . . the main reason we came to your restaurant was for the belly dancers and missed the first show as we were not seated yet and the 2nd belly dancer only danced at two tables in the back of the restaurant and never made it around to the other half of the restaurant .']", "output": "[['NULL', 'service general', 'negative', 'never made it around']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But the service is HORRID !']", "output": "[['service', 'service general', 'negative', 'HORRID']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I am actually offended to have spent so much money on such a bad experience .']", "output": "[['NULL', 'restaurant general', 'negative', 'bad'], ['NULL', 'restaurant prices', 'negative', 'so much money']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's somewhere you can eat and be happy .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'happy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"We 've tried before but it always packed and doesn 't take reservations .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'neutral', 'always packed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I think that it is absolutely brilliant and well runned business operation .']", "output": "[['NULL', 'restaurant general', 'positive', 'brilliant']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Prices are fair across the board for both food and bev .']", "output": "[['food', 'food prices', 'positive', 'fair'], ['bev', 'drinks prices', 'positive', 'fair']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They refuse to seat parties of 3 or more on weekends .']", "output": "[['NULL', 'service general', 'negative', 'refuse']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Awesome']", "output": "[['NULL', 'restaurant general', 'positive', 'Awesome']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If you \u2019 re planning to come here , make sure that your date is someone whom you really like since you \u2019 ll be ushered to private booths where there will be no people or food watching ( choose the ones on the ground level that have glass ceilings so you may see the stars in the sky ! ) .']", "output": "[['private booths', 'ambience general', 'positive', 'ushered']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The food was great and tasty , but the sitting space was too small , I do n't like being cramp in a corner .\"]", "output": "[['food', 'food quality', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty'], ['sitting space', 'ambience general', 'negative', 'too small']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have never been so disgusted by both food an service .']", "output": "[['food', 'food quality', 'negative', 'disgusted'], ['service', 'service general', 'negative', 'disgusted']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Very affordable and excellent ambient !']", "output": "[['ambient', 'ambience general', 'positive', 'excellent'], ['NULL', 'restaurant prices', 'positive', 'affordable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['To me it exemplifies Soho , cute , artsy , interesting .']", "output": "[['NULL', 'ambience general', 'positive', 'cute'], ['NULL', 'ambience general', 'positive', 'artsy'], ['NULL', 'ambience general', 'positive', 'interesting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .']", "output": "[['service', 'service general', 'negative', 'disappointed'], ['food', 'food prices', 'negative', 'pricey'], ['food', 'food quality', 'negative', 'overated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Once you step into Cosette , you 're miraculously in a small , off-the-beaten path Parisian bistro .\"]", "output": "[['Cosette', 'ambience general', 'positive', 'off-the-beaten']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was pleasantly suprised .']", "output": "[['NULL', 'restaurant general', 'positive', 'pleasantly suprised']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great Atmosphere']", "output": "[['Atmosphere', 'ambience general', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The rice was poor quality and was cooked so badly it was hard .']", "output": "[['rice', 'food quality', 'negative', 'poor quality'], ['rice', 'food quality', 'negative', 'cooked so badly'], ['rice', 'food quality', 'negative', 'hard']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We will return many times for this oasis in mid-town .']", "output": "[['NULL', 'restaurant general', 'positive', 'return']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['For a restaurant with such a good reputation and that is usually so packed , there was no reason for such a lack of intelligent customer service .']", "output": "[['restaurant', 'restaurant miscellaneous', 'positive', 'good reputation'], ['customer service', 'service general', 'negative', 'intelligent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\"]", "output": "[['setting', 'ambience general', 'positive', 'intimate'], ['service', 'service general', 'positive', 'nice'], ['NULL', 'restaurant miscellaneous', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['this is can became on e of the NY Italian Food fare institutions .']", "output": "[['NULL', 'restaurant general', 'positive', 'fare institutions']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I think I 've had some the best meals of my life at minnow .\"]", "output": "[['meals', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Stepping into Casa La Femme last night was a true experience unlike any other in New York !']", "output": "[['Casa La Femme', 'restaurant general', 'positive', 'true']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['There is a lot of variety even for people who eat vegetarian like me .']", "output": "[['NULL', 'food style_options', 'positive', 'a lot of variety']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['it was really good pizza .']", "output": "[['pizza', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['On a hot day it was fabulous to stop in and enjoy lunch .']", "output": "[['NULL', 'restaurant general', 'positive', 'fabulous'], ['NULL', 'restaurant general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is the MOST wonderful restaurant in all of New York City , not just Brooklyn ...']", "output": "[['restaurant', 'restaurant general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This little place definitely exceeded my expectations and you sure get a lot of food for your money .']", "output": "[['food', 'food style_options', 'positive', 'lot'], ['place', 'restaurant general', 'positive', 'exceeded my expectations'], ['food', 'food prices', 'positive', 'lot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !']", "output": "[['terrace', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place is a BISTRO which means : simple dishes and wine served efficiently in a bustling atmosphere .']", "output": "[['dishes', 'food style_options', 'positive', 'simple'], ['NULL', 'service general', 'positive', 'served efficiently'], ['atmosphere', 'ambience general', 'positive', 'bustling']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was visiting New York City with a friend and we discovered this really warm and inviting restaurant .']", "output": "[['restaurant', 'ambience general', 'positive', 'inviting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The dinner was ok , nothing I would have again .']", "output": "[['dinner', 'food quality', 'negative', 'ok']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My friend from Milan and myself were pleasantly surprised when we arrived and everyone spoke italian .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'pleasantly surprised']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We asked for sides which the waiter than admitted that he forgot to put in that part of our order .']", "output": "[['waiter', 'service general', 'negative', 'forgot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Also , because it is so thin , it gets cold very quickly and its not that filling .']", "output": "[['NULL', 'food quality', 'negative', 'gets cold very quickly'], ['NULL', 'food style_options', 'negative', 'not that filling']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The shrimp scampi was excellent and the antipasti were plentiful .']", "output": "[['shrimp scampi', 'food quality', 'positive', 'excellent'], ['antipasti', 'food style_options', 'positive', 'plentiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .']", "output": "[['chicken pot pie', 'food quality', 'positive', 'exceptional'], ['cheeseburger', 'food style_options', 'positive', 'huge'], ['cheeseburger', 'food quality', 'positive', 'delictable'], ['service', 'service general', 'positive', 'professional'], ['service', 'service general', 'positive', 'warm']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Some Pineapple covered in a glaze of some kind and some pear tart thing Not impressive at all .']", "output": "[['NULL', 'food style_options', 'negative', 'covered in a glaze of some kin'], ['NULL', 'food quality', 'negative', 'Not impressive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Save yourself the time and trouble and skip this one !']", "output": "[['NULL', 'restaurant general', 'negative', 'skip']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was authentic .']", "output": "[['food', 'food quality', 'positive', 'authentic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It was pretty inexpensive too .']", "output": "[['NULL', 'restaurant prices', 'positive', 'inexpensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We thought the dessert would be better , Wrong !']", "output": "[['dessert', 'food quality', 'negative', 'Wrong']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sushi is amazing ! ! !']", "output": "[['sushi', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was good and food is wonderful .']", "output": "[['Service', 'service general', 'positive', 'good'], ['food', 'food quality', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You must try the shrimp appetizers .']", "output": "[['shrimp appetizers', 'food quality', 'positive', 'try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\"]", "output": "[['NULL', 'food style_options', 'negative', 'eaten several more'], ['portion', 'food style_options', 'positive', 'fine'], ['french fries', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['for 7 years they have put out the most tasty , most delicious food and kept it that way ...']", "output": "[['food', 'food quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The mussels were fantastic and so was the dessert ... definitely going to be back very soon .']", "output": "[['mussels', 'food quality', 'positive', 'fantastic'], ['dessert', 'food quality', 'positive', 'fantastic'], ['NULL', 'restaurant general', 'positive', 'going to be back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .']", "output": "[['wines', 'drinks quality', 'negative', 'above average'], ['wines', 'drinks prices', 'negative', 'above average']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['There was no tap beer that evening , which was a disappointment .']", "output": "[['beer', 'drinks style_options', 'negative', 'disappointment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This has got to be one of the most overrated restaurants in Brooklyn .']", "output": "[['NULL', 'restaurant general', 'negative', 'overrated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Hats off to the chef .']", "output": "[['chef', 'food quality', 'positive', 'Hats off']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Love Al Di La']", "output": "[['Al Di La', 'restaurant general', 'positive', 'Love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Yes , the place is classy and beautiful , but they most certainly target the uber whealthy Not the common joe that wants to go all out every once in a while .']", "output": "[['place', 'ambience general', 'positive', 'classy'], ['place', 'ambience general', 'positive', 'beautiful'], ['place', 'restaurant prices', 'negative', 'target the uber whealthy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The baked clams octopus we shared as appetizers were the best we 've ever had ! !\"]", "output": "[['baked clams octopus', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The floor was wet , the trash can filled with hand towels n all over the floor , no soap , and no hand towels left .']", "output": "[['NULL', 'ambience general', 'negative', 'no hand towels left']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If we were to move from the upper east side , we would genuinely miss this restaurant .']", "output": "[['restaurant', 'restaurant general', 'positive', 'miss']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !']", "output": "[['crust', 'food quality', 'positive', 'best'], ['pizza', 'food quality', 'positive', 'should not have additional toppings']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best Italian food I ever had ( and being Italian , that means alot ) .']", "output": "[['Italian food', 'food quality', 'positive', 'Best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I LOOOVE their eggplant pizza , as well as their pastas !']", "output": "[['eggplant pizza', 'food quality', 'positive', 'LOOOVE'], ['pastas', 'food quality', 'positive', 'LOOOVE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Who has room for Cheesesticks with the best pizza in NYC !']", "output": "[['pizza', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The characters really make for an enjoyable experience .']", "output": "[['characters', 'ambience general', 'positive', 'enjoyable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It looked like shredded cheese partly done - still in strips .']", "output": "[['NULL', 'food quality', 'negative', 'shredded cheese partly done']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\"]", "output": "[['host', 'service general', 'positive', 'amazing'], ['crew', 'service general', 'positive', 'treated me as family']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The quantity is also very good , you will come out satisfied .']", "output": "[['quantity', 'food style_options', 'positive', 'good'], ['quantity', 'food style_options', 'positive', 'satisfied']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Went there with my wife and we had to wait for a table even though you could see there many that were empty with not reservation sigh on them .']", "output": "[['NULL', 'service general', 'negative', 'wait']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['On the way out , we heard of other guests complaining about similar issues .']", "output": "[['NULL', 'service general', 'negative', 'complaining']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is one of the best comfort food places in the city .']", "output": "[['NULL', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were ushered to the bar to wait momentarily and upon arrival were so excited .']", "output": "[['NULL', 'restaurant general', 'positive', 'excited']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was quick .']", "output": "[['Service', 'service general', 'positive', 'quick']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"its a little out of the way if you do n't live in the neighborhood , but definitely worth the trip from wherever you are .\"]", "output": "[['NULL', 'location general', 'negative', 'a little out of the way'], ['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A coworker and I tried Pacifico after work a few Fridays and loved it .']", "output": "[['Pacifico', 'restaurant general', 'positive', 'loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waitress was not attentive at all .']", "output": "[['waitress', 'service general', 'negative', 'not attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"If you do n't mind pre-sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\"]", "output": "[['fish', 'food quality', 'negative', 'low quality'], ['staff', 'service general', 'negative', 'unfriendly'], ['sushi chef', 'restaurant miscellaneous', 'negative', 'miserable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Furthermore , the rice had no seasoning , so the sushi was bland and disgusting .']", "output": "[['rice', 'food quality', 'negative', 'no seasoning'], ['sushi', 'food quality', 'negative', 'bland'], ['sushi', 'food quality', 'negative', 'disgusting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The first time I went , and was completely taken by the live jazz band and atmosphere , I ordered the Lobster Cobb Salad .']", "output": "[['live jazz band', 'ambience general', 'positive', 'taken'], ['atmosphere', 'ambience general', 'positive', 'taken']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had their eggs benedict for brunch , which were the worst in my entire life , I tried removing the hollondaise sauce completely that was how failed it was .']", "output": "[['eggs benedict', 'food quality', 'negative', 'worst']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Kind of a small place but I guess if they are not too busy might be able to fit a group or kids .']", "output": "[['place', 'restaurant miscellaneous', 'neutral', 'small']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My Girlfriend and I stumbled onto this hopping place the other night and had a great time !']", "output": "[['place', 'restaurant general', 'positive', 'hopping'], ['place', 'restaurant general', 'positive', 'great time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The hot dogs are top notch , and they 're Slamwich is amazing !\"]", "output": "[['hot dogs', 'food quality', 'positive', 'top notch'], ['Slamwich', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['skip dessert .']", "output": "[['dessert', 'food quality', 'negative', 'skip']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was quick and friendly .']", "output": "[['service', 'service general', 'positive', 'quick']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .']", "output": "[['Chow fun', 'food quality', 'negative', 'dry'], ['pork shu mai', 'food quality', 'negative', 'greasy'], ['NULL', 'ambience general', 'negative', 'loud'], ['NULL', 'ambience general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Yes , they use fancy ingredients , but even fancy ingredients do n't make for good pizza unless someone knows how to get the crust right .\"]", "output": "[['ingredients', 'food quality', 'positive', 'fancy'], ['pizza', 'food quality', 'negative', \"do n't make for good pizza\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Authentic Pakistani food .']", "output": "[['Pakistani food', 'food quality', 'positive', 'Authentic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was actually aweful .']", "output": "[['food', 'food quality', 'negative', 'aweful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is by far my favorite place in the neighborhood .']", "output": "[['place', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This restaurant was way overhyped .']", "output": "[['restaurant', 'restaurant general', 'negative', 'overhyped']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best restaurant in Brooklyn']", "output": "[['restaurant', 'restaurant general', 'positive', 'Best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was slow , but the people were friendly .']", "output": "[['Service', 'service general', 'negative', 'slow'], ['people', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['However , I think Jeckll and Hydes t is one of those places that is fun to do once .']", "output": "[['Jeckll and Hydes', 'restaurant general', 'positive', 'fun']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The fish was really , really fresh .']", "output": "[['fish', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had a grat time at Jekyll and Hyde !']", "output": "[['Jekyll and Hyde', 'restaurant general', 'positive', 'had a grat time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"On that scale , it 's a world-beater .\"]", "output": "[['NULL', 'restaurant general', 'negative', 'world-beater']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the drinks are amazing and half off till 8pm .']", "output": "[['drinks', 'drinks quality', 'positive', 'amazing'], ['drinks', 'drinks prices', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've waited over one hour for food .\"]", "output": "[['NULL', 'service general', 'negative', 'waited over one hour']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .']", "output": "[['food', 'food quality', 'negative', 'spicy'], ['spice', 'food quality', 'negative', 'could barely eat it']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Haru on Park S is simply disgusting .']", "output": "[['Haru on Park S', 'restaurant general', 'negative', 'disgusting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"those rolls were big , but not good and sashimi was n't fresh .\"]", "output": "[['rolls', 'food style_options', 'positive', 'big'], ['rolls', 'food quality', 'negative', 'not good'], ['sashimi', 'food quality', 'negative', \"was n't fresh\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is really trendi but they have forgotten about the most important part of a restaurant , the food .']", "output": "[['food', 'food quality', 'negative', 'forgotten'], ['place', 'ambience general', 'positive', 'trendi']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Pacifico is a great place to casually hang out .']", "output": "[['Pacifico', 'restaurant miscellaneous', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were not dissappointed in the least bit by this little gem .']", "output": "[['NULL', 'restaurant general', 'positive', 'not dissappointed in the least bit']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I attended a holiday dinner at the restaurant , and the food was majorly disappointing .']", "output": "[['food', 'food quality', 'negative', 'disappointing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You should pass on the calamari .']", "output": "[['calamari', 'food quality', 'negative', 'pass']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['His response was smug , arrogant , and condescending , totally consistent with his deportment on display all evening .']", "output": "[['NULL', 'service general', 'negative', 'smug'], ['NULL', 'service general', 'negative', 'arrogant'], ['NULL', 'service general', 'negative', 'condescending']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"If you 've ever been along the river in Weehawken you have an idea of the top of view the chart house has to offer .\"]", "output": "[['view', 'location general', 'positive', 'the top of view the chart house has to offer']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only thing more wonderful than the food ( which is exceptional ) is the service .']", "output": "[['food', 'food quality', 'positive', 'exceptional'], ['service', 'service general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"short and sweet \u2013 seating is great : it 's romantic , cozy and private .\"]", "output": "[['seating', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['She was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .']", "output": "[['NULL', 'service general', 'positive', 'helpful'], ['dishes', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The rolls are creative and I have yet to find another sushi place that serves up more inventive yet delicious japanese food .']", "output": "[['rolls', 'food style_options', 'positive', 'creative'], ['japanese food', 'food style_options', 'positive', 'inventive'], ['japanese food', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I have it a 4 instead of 5 because of the price ( just chicken tikka masala - no bread of rice - is $ 25 ) , which I would expect at a upscale Indian restaurant but this place doesn 't have an upscale feel .\"]", "output": "[['place', 'restaurant general', 'positive', '4 instead of 5'], ['chicken tikka masala', 'food prices', 'negative', 'no bread of rice'], ['feel', 'ambience general', 'negative', \"doesn 't have an upscale feel\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The pizza is overpriced and soggy .']", "output": "[['pizza', 'food quality', 'negative', 'soggy'], ['pizza', 'food prices', 'negative', 'overpriced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food looked very appetizing and delicious since it came on a variety of fancy plates .']", "output": "[['food', 'food style_options', 'positive', 'appetizing'], ['food', 'food style_options', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"All in all we 're already coming up with excuses to go ahead really soon in the next few wks ! ! ! !\"]", "output": "[['NULL', 'restaurant general', 'positive', 'go ahead really soon']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Hands down the best pizza on the planet .']", "output": "[['pizza', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['never swaying , never a bad meal , never bad service ...']", "output": "[['NULL', 'restaurant general', 'positive', 'never swaying'], ['meal', 'food quality', 'positive', 'never a bad'], ['service', 'service general', 'positive', 'never bad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The martinis are amazing and very fairly priced .']", "output": "[['martinis', 'drinks quality', 'positive', 'amazing'], ['martinis', 'drinks prices', 'positive', 'fairly priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Despite the confusing mirrors this will likely be my go-to for modern Japanese food for the foreseeable future .']", "output": "[['modern Japanese food', 'food quality', 'positive', 'go-to for'], ['mirrors', 'ambience general', 'negative', 'confusing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\"]", "output": "[['food', 'food quality', 'positive', 'great'], ['food', 'food prices', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Avoid this place !']", "output": "[['place', 'restaurant general', 'negative', 'Avoid']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Quite frankly , this is some of the worst sushi I have ever tried .']", "output": "[['sushi', 'food quality', 'negative', 'worst']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Such a disappointment ...']", "output": "[['NULL', 'service general', 'negative', 'disappointment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was one of the people that went for this horrible experience .']", "output": "[['NULL', 'restaurant general', 'negative', 'horrible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Definitely worth the trip to Battery Park City !']", "output": "[['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would definitely recommend SEA if you like thai cuisine !']", "output": "[['thai cuisine', 'food quality', 'positive', 'like']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have eaten at Saul , many times , the food is always consistently , outrageously good .']", "output": "[['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Big Wong gets big Ups for a fine establishment .']", "output": "[['Big Wong', 'restaurant general', 'positive', 'big Ups'], ['Big Wong', 'restaurant general', 'positive', 'fine']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lamb was tender so full of flavor , the dessert was divine ! !']", "output": "[['lamb', 'food quality', 'positive', 'tender'], ['dessert', 'food quality', 'positive', 'divine']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['THe Pizza and wine were excellent -- the service too -- but what really MADE this place was the backyard dining area .']", "output": "[['Pizza', 'food quality', 'positive', 'excellent'], ['wine', 'drinks quality', 'positive', 'excellent'], ['service', 'service general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Only complaint is the pricing -- I believe it would be more reasonable to pay a dollar less on each item listed on the menu .']", "output": "[['menu', 'food prices', 'negative', 'complaint']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The level of rudeness was preposterous .']", "output": "[['NULL', 'service general', 'negative', 'rudeness']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was also very good .']", "output": "[['Service', 'service general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try the lobster teriyaki and the rose special roll .']", "output": "[['lobster teriyaki', 'food quality', 'positive', 'Try'], ['rose special roll', 'food quality', 'positive', 'Try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try everything for that matter , it is all good .']", "output": "[['NULL', 'food quality', 'positive', 'all good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try it !']", "output": "[['NULL', 'restaurant general', 'positive', 'Try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They have it all -- great price , food , and service .']", "output": "[['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The blond wood decor is very soothing , the premium sake is excellent and the service is great .']", "output": "[['blond wood decor', 'ambience general', 'positive', 'soothing'], ['premium sake', 'drinks quality', 'positive', 'soothing'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is great .']", "output": "[['place', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'll be back for sure .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'be back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everything we had was good or ok ... but definitely nothing great .']", "output": "[['NULL', 'food quality', 'neutral', 'nothing great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Again , no apology , no is there anything else I can get you , no can I get you a drink to make up for it , nothing ! ! ! !']", "output": "[['NULL', 'service general', 'negative', 'no apology']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Watch the talented belly dancers as you enjoy delicious baba ganoush that 's more lemony than smoky .\"]", "output": "[['baba ganoush', 'food quality', 'positive', 'enjoy delicious'], ['belly dancers', 'ambience general', 'positive', 'talented']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\"]", "output": "[['Service', 'service general', 'positive', 'devine'], ['oysters', 'food quality', 'positive', 'sensual'], ['NULL', 'restaurant prices', 'positive', \"can't be beat\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wonderful at holiday time .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'Wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is terrific , as is the value .']", "output": "[['NULL', 'food quality', 'positive', 'terrific'], ['NULL', 'food prices', 'positive', 'terrific']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Overall I would recommend it and go back again .']", "output": "[['NULL', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['but overall i give it a 10']", "output": "[['NULL', 'restaurant general', 'positive', 'give it a 10']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I go and eat out at many different restaurants and this is one place you have go and try .']", "output": "[['place', 'restaurant general', 'positive', 'have go and try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Friendly staff that actually lets you enjoy your meal and the company you 're with .\"]", "output": "[['staff', 'service general', 'positive', 'Friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's great to go for a quick lunch either alone or with a friend .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Was surprisingly disappointed .']", "output": "[['NULL', 'food quality', 'negative', 'surprisingly disappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Zero ambiance to boot .']", "output": "[['ambiance', 'ambience general', 'negative', 'Zero']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sake menu should not be overlooked !']", "output": "[['sake menu', 'drinks style_options', 'positive', 'overlooked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Very `` normal Indian food `` , but done really well .']", "output": "[['Indian food', 'food style_options', 'neutral', 'normal'], ['Indian food', 'food quality', 'positive', 'well']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\"]", "output": "[['delivery guys', 'service general', 'negative', 'downside']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['but the service was a bit slow .']", "output": "[['service', 'service general', 'negative', 'slow']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best of all is the warm vibe , the owner is super friendly and service is fast .']", "output": "[['vibe', 'ambience general', 'positive', 'warm'], ['owner', 'service general', 'positive', 'friendly'], ['service', 'service general', 'positive', 'fast']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The main entree was also very good .']", "output": "[['main entree', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Also , I personally was n't a fan of the portobello and asparagus mole .\"]", "output": "[['portobello and asparagus mole', 'food quality', 'negative', 'fan']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It doesn 't look appetizing as it 's covered in squid ink and it turns your lips and teeth black , but the taste was phenomenal .\"]", "output": "[['NULL', 'food style_options', 'negative', \"doesn 't look appetizing\"], ['NULL', 'food quality', 'positive', 'phenomenal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .']", "output": "[['scallops', 'food quality', 'positive', 'like'], ['mahi mahi ( on saffron risotto', 'food quality', 'positive', 'like']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Try their chef 's specials -- they are to die for .\"]", "output": "[[\"chef 's specials\", 'food quality', 'positive', 'Try'], [\"chef 's specials\", 'food quality', 'positive', 'die for']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It was the first place we ate on our first trip to New York , and it will be the last place we stop as we head out of town on our next trip to New York .']", "output": "[['place', 'restaurant miscellaneous', 'positive', 'the last place we stop']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Personal pans are the perfect size for those hungry nights .']", "output": "[['Personal pans', 'food style_options', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['IT WAS HORRIBLE .']", "output": "[['NULL', 'restaurant general', 'negative', 'HORRIBLE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Very immature bartender , didnt know how to make specific drinks , service was so slowwwww , the food was not fresh or warm , waitresses were busy flirting with men at the bar and werent very attentive to all the customers .']", "output": "[['bartender', 'service general', 'negative', 'immature'], ['service', 'service general', 'negative', 'slowwwww'], ['food', 'food quality', 'negative', 'not fresh or warm'], ['waitresses', 'service general', 'negative', 'werent very attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The pastas are incredible , the risottos ( particularly the sepia ) are fantastic and the braised rabbit is amazing .']", "output": "[['pastas', 'food quality', 'positive', 'incredible'], ['risottos', 'food quality', 'positive', 'fantastic'], ['sepia', 'food quality', 'positive', 'fantastic'], ['braised rabbit', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['amazing fun for hot dog lovers of all ages PLEASE do yourself a favor and check this place out ! ! ! !']", "output": "[['place', 'restaurant general', 'positive', 'check this place out'], ['hot dog', 'food quality', 'positive', 'amazing fun']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .']", "output": "[['sushi', 'food quality', 'positive', 'particular'], ['ceviche mix ( special )', 'food quality', 'positive', 'please'], ['crab dumplings', 'food quality', 'positive', 'please'], ['assorted sashimi', 'food quality', 'positive', 'please'], ['sushi', 'food quality', 'positive', 'particular'], ['rolls', 'food quality', 'positive', 'please'], ['two types of sake', 'drinks quality', 'positive', 'please'], ['banana tempura', 'food quality', 'positive', 'please']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Well , this place is so Ghetto its not even funny .']", "output": "[['place', 'ambience general', 'negative', 'Ghetto'], ['place', 'ambience general', 'negative', 'not even funny']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .']", "output": "[['Gulab Jamun ( dessert )', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good food : my favorite is the seafood spaghetti .']", "output": "[['food', 'food quality', 'positive', 'Good'], ['seafood spaghetti', 'food quality', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['In the end you end up with a fair tab and NOTHING BUT A GREAT TIME ! ! !']", "output": "[['NULL', 'restaurant prices', 'positive', 'fair'], ['NULL', 'restaurant general', 'positive', 'GREAT']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I will go back to Suan soon !']", "output": "[['Suan', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great bagels made the old-fashioned way .']", "output": "[['bagels', 'food quality', 'positive', 'Great'], ['bagels', 'food style_options', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Had no flavor and the staff is rude and not attentive .']", "output": "[['staff', 'service general', 'negative', 'rude'], ['staff', 'service general', 'negative', 'not attentive'], ['NULL', 'food quality', 'negative', 'no flavor']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The design of the space is good .']", "output": "[['space', 'ambience general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I have tried to make reservations , but both times , the hostess did n't have my name .\"]", "output": "[['hostess', 'service general', 'negative', \"did n't have my name\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"When you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside .\"]", "output": "[['main dining room', 'ambience general', 'positive', 'spectacular'], ['ceiling', 'ambience general', 'positive', 'spectacular'], ['ceiling', 'ambience general', 'positive', 'hand-painted high']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['honestly the worst sushi my husband and i had in our entire lives .']", "output": "[['sushi', 'food quality', 'negative', 'worst']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Big Wong is a great place to eat and fill your stomach .']", "output": "[['Big Wong', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The drinks are always welll made and wine selection is fairly priced .']", "output": "[['drinks', 'drinks quality', 'positive', 'welll made'], ['wine selection', 'drinks prices', 'positive', 'fairly priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service is fast and friendly .']", "output": "[['Service', 'service general', 'positive', 'fast'], ['Service', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .']", "output": "[['Traditional French decour', 'ambience general', 'positive', 'pleasant'], ['hall', 'ambience general', 'negative', 'noisy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This small Astoria souvlaki spot makes what many consider the best gyros in New York .']", "output": "[['gyros', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The prices are wonderfully low .']", "output": "[['NULL', 'restaurant prices', 'positive', 'wonderfully low']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['By the time we finished our dinner we still had not received one beverage NOR hooka !']", "output": "[['NULL', 'service general', 'negative', 'still had not received']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['After the 4th time i asked again and the waiter than said after our dinner .']", "output": "[['waiter', 'service general', 'negative', 'asked again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['im not necessarily fanatical about this place , but it was a fun time for low pirces .']", "output": "[['place', 'restaurant general', 'positive', 'fanatical'], ['place', 'restaurant prices', 'positive', 'fanatical']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['BEST DINING EXPERIENCE IN THE WEST VILLAGE !']", "output": "[['NULL', 'restaurant general', 'positive', 'BEST']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"This was a repeat visit and we 'll definitely be back again .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'be back again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sandwiches are dry , tasteless and way overpriced .']", "output": "[['sandwiches', 'food quality', 'negative', 'dry'], ['sandwiches', 'food quality', 'negative', 'tasteless'], ['sandwiches', 'food prices', 'negative', 'overpriced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Go to Volare for 1st class service and terrific food .']", "output": "[['service', 'service general', 'positive', '1st class'], ['food', 'food quality', 'positive', 'terrific']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The dosas are skimpy , unattractive and drip with grease , and personally I 'd drink popcorn topping before I 'd eat another one of these .\"]", "output": "[['dosas', 'food style_options', 'negative', 'skimpy'], ['dosas', 'food quality', 'negative', 'unattractive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Would NEVER go back']", "output": "[['NULL', 'restaurant general', 'negative', 'NEVER go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Definately check it out ! ! !']", "output": "[['NULL', 'restaurant general', 'positive', 'check it out']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['whoever the jazz duo was , they were on POINT .']", "output": "[['jazz duo', 'ambience general', 'positive', 'on POINT']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"We did n't want a bottle of bubbly on a weekday so we each got little bottles of Korbett it was just enough .\"]", "output": "[['bottles of Korbett', 'drinks style_options', 'positive', 'enough']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Favorite Sushi in NYC']", "output": "[['Sushi', 'food quality', 'positive', 'Favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Both times I was extremely dissappointed by the service , which was boarderline rude .']", "output": "[['service', 'service general', 'negative', 'dissappointed'], ['service', 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Mermaid Inn is an overall good restaurant with really good seafood .']", "output": "[['seafood', 'food quality', 'positive', 'good'], ['Mermaid Inn', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['By the time we left our wallets were empy and so were our stomachs AND we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !']", "output": "[['food', 'food quality', 'negative', 'our wallets were empy and so were our stomachs'], ['NULL', 'restaurant prices', 'negative', 'left our wallets were empy'], ['NULL', 'restaurant miscellaneous', 'negative', 'missed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Have recommended the place to friends , always gets good response .']", "output": "[['place', 'restaurant miscellaneous', 'positive', 'recommended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The combination of super-fresh ingredients in the dishes are unusual but really delicious .']", "output": "[['ingredients', 'food quality', 'positive', 'super-fresh'], ['ingredients', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I am reluctant to write because I would not want my jem of a pizza place to become overcrowded .']", "output": "[['pizza place', 'restaurant miscellaneous', 'positive', 'overcrowded']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I recieved prompt service with a smile .']", "output": "[['service', 'service general', 'positive', 'prompt']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try the crunchy tuna , it is to die for .']", "output": "[['crunchy tuna', 'food quality', 'positive', 'Try'], ['crunchy tuna', 'food quality', 'positive', 'die for']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You will find yourself returning quite often .']", "output": "[['NULL', 'restaurant general', 'positive', 'returning quite often']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Baluchi 's has solid food and a nice decor at reasonable prices .\"]", "output": "[['food', 'food quality', 'positive', 'solid'], ['decor', 'ambience general', 'positive', 'nice'], [\"Baluchi 's\", 'restaurant prices', 'positive', 'solid']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service here was great , food was fantastic .']", "output": "[['Service', 'service general', 'positive', 'great'], ['food', 'food quality', 'positive', 'fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great Shabu Shabu']", "output": "[['Shabu Shabu', 'food quality', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When the main course finally arrived ( another 45mins ) half of our order was missing .']", "output": "[['NULL', 'service general', 'negative', 'missing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I loved it and would HIGHLY RECOMMEND .']", "output": "[['NULL', 'restaurant general', 'positive', 'loved'], ['NULL', 'restaurant general', 'positive', 'HIGHLY RECOMMEND']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['An unpretentious spot in Park Slope , the sushi is consistently good , the service is pleasant , effective and unassuming .']", "output": "[['spot', 'ambience general', 'positive', 'unpretentious'], ['sushi', 'food quality', 'positive', 'good'], ['service', 'service general', 'positive', 'pleasant'], ['service', 'service general', 'positive', 'effective'], ['service', 'service general', 'positive', 'unassuming']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['In the summer months , the back garden area is really nice .']", "output": "[['back garden area', 'ambience general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the all-u-can-eat sushi is definitely in very poor quality .']", "output": "[['all-u-can-eat sushi', 'food quality', 'negative', 'poor quality']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend the fish tacos , everything else was ok .']", "output": "[['fish tacos', 'food quality', 'positive', 'recommend'], ['NULL', 'food quality', 'neutral', 'ok']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is definitely a good spot for snacks and chat .']", "output": "[['spot', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I book a gorgeous white organza tent which included a four course prix fix menu which we enjoyed a lot .']", "output": "[['four course prix fix menu', 'food quality', 'positive', 'enjoyed'], ['white organza tent', 'ambience general', 'positive', 'gorgeous']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I would recommend Roxy 's for that , but not for their food .\"]", "output": "[['food', 'food quality', 'negative', 'recommend'], ['NULL', 'food quality', 'negative', 'not for their food']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The mussles were the fishiest things I 've ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w/ chicken had bones in it ... It was disgusting .\"]", "output": "[['mussles', 'food quality', 'negative', 'fishiest'], ['seabass', 'food quality', 'negative', 'bland'], ['goat cheese salad', 'food quality', 'negative', 'missing'], ['penne w/ chicken', 'food quality', 'negative', 'disgusting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Make sure you try this place as often as you can .']", "output": "[['place', 'restaurant general', 'positive', 'try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I am not a vegetarian but , almost all the dishes were great .']", "output": "[['dishes', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food and the prices are very reasonable .']", "output": "[['food', 'food quality', 'positive', 'Great'], ['NULL', 'restaurant prices', 'positive', 'reasonable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Prices are very good .']", "output": "[['NULL', 'restaurant prices', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Go here for a romantic dinner but not for an all out wow dining experience .']", "output": "[['NULL', 'ambience general', 'positive', 'romantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I definitely wouldn 't go back .\"]", "output": "[['NULL', 'restaurant general', 'negative', \"wouldn 't go back\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine list is extensive and impressive .']", "output": "[['wine list', 'drinks style_options', 'positive', 'extensive'], ['wine list', 'drinks style_options', 'positive', 'impressive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wine list is extensive without being over-priced .']", "output": "[['Wine list', 'drinks style_options', 'positive', 'extensive without being over-priced'], ['Wine list', 'drinks prices', 'positive', 'extensive without being over-priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .']", "output": "[['outdoor seating', 'ambience general', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was ok and fair nothing to go crazy .']", "output": "[['food', 'food quality', 'neutral', 'ok']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I also ordered the Change Mojito , which was out of this world .']", "output": "[['Change Mojito', 'drinks quality', 'positive', 'out of this world']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['( Always ask the bartender for the SEASONAL beer ! ! !']", "output": "[['SEASONAL beer', 'drinks quality', 'positive', 'Always ask']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .']", "output": "[['meat', 'food quality', 'positive', 'fresh'], ['sauces', 'food quality', 'positive', 'great'], ['service', 'service general', 'positive', 'good'], ['kimchi', 'food prices', 'positive', 'free'], ['salad', 'food prices', 'positive', 'free']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The pizza was great .']", "output": "[['pizza', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is the best sushi in new york city - hands down .']", "output": "[['sushi', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food here is rather good , but only if you like to wait for it .']", "output": "[['food', 'food quality', 'positive', 'good'], ['NULL', 'service general', 'negative', 'wait for it']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Raymond the bartender rocks !']", "output": "[['Raymond', 'service general', 'positive', 'rocks']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Too bad the food was n't of the same heritage .\"]", "output": "[['food', 'food quality', 'negative', 'bad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff offers impeccable service .']", "output": "[['staff', 'service general', 'positive', 'impeccable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had the Lamb special which was perfect .']", "output": "[['Lamb special', 'food quality', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Waitstaff are very friendly .']", "output": "[['Waitstaff', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The portion sizes here are huge , and the sushi is good .']", "output": "[['portion sizes', 'food style_options', 'positive', 'huge'], ['sushi', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Enjoyed a very nice Caesar Salad while my wife had arugula and goat cheese ... both very tasty .']", "output": "[['Caesar Salad', 'food quality', 'positive', 'Enjoyed'], ['Caesar Salad', 'food quality', 'positive', 'nice'], ['arugula and goat cheese', 'food quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We concluded with tiramisu chocolate cake , both were delicious .']", "output": "[['tiramisu chocolate cake', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I will NEVER return .']", "output": "[['NULL', 'restaurant general', 'negative', 'NEVER return']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The food 's as good as ever .\"]", "output": "[['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The rest of the dim sum , though pricey by Chinatown standards , is worth it .']", "output": "[['dim sum', 'food prices', 'negative', 'pricey'], ['dim sum', 'food quality', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Beef noodle soup is good as well .']", "output": "[['Beef noodle soup', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['cirspy crust margherita pizza']", "output": "[['margherita pizza', 'food quality', 'positive', 'cirspy crust']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food , amazing service , this place is a class act .']", "output": "[['food', 'food quality', 'positive', 'Great'], ['service', 'service general', 'positive', 'amazing'], ['place', 'restaurant general', 'positive', 'class act']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"All the various Greek and Cypriot dishes are excellent , but the gyro is the reason to come -- if you do n't eat one your trip was wasted .\"]", "output": "[['Greek and Cypriot dishes', 'food quality', 'positive', 'excellent'], ['gyro', 'food quality', 'positive', 'the reason to come']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Fresh ingredients and everything is made to order .']", "output": "[['ingredients', 'food quality', 'positive', 'Fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the pad se ew chicken was delicious , however the pad thai was far too oily .']", "output": "[['pad se ew chicken', 'food quality', 'positive', 'delicious'], ['pad thai', 'food quality', 'negative', 'oily']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The Prix Fixe menu is worth every penny and you get more than enough ( both in quantity AND quality ) .']", "output": "[['Prix Fixe menu', 'food quality', 'positive', 'worth'], ['Prix Fixe menu', 'food style_options', 'positive', 'worth'], ['Prix Fixe menu', 'food prices', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It \u2019 s just you and your date and an occasional cute \u2018 excuse me \u2019 before the waiter opens the little curtain to your booth !']", "output": "[['waiter', 'service general', 'positive', 'cute']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Nice for one time special occasion .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'Nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Somehow working the italian charm with constant mille grazie does not constitute proper service .']", "output": "[['service', 'service general', 'negative', 'not constitute proper']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"they were dry and disgusting , i did n't even finish my first piece .\"]", "output": "[['NULL', 'food quality', 'negative', 'dry'], ['NULL', 'food quality', 'negative', 'disgusting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is a lot of fun with live entertainment and all kinds of Disney type special effects .']", "output": "[['NULL', 'ambience general', 'positive', 'fun']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Nice atmosphere , the service was very pleasant and the desert was good .']", "output": "[['atmosphere', 'ambience general', 'positive', 'Nice'], ['service', 'service general', 'positive', 'pleasant'], ['desert', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['After that she simply took our plates , walked away , came back another TWENTY minutes later with the bill and the chicken on it ! ! ! ! ! ! ! ! ! ! ! !']", "output": "[['NULL', 'service general', 'negative', 'TWENTY minutes later']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's just average , just shredded , no seasoning on it .\"]", "output": "[['NULL', 'food quality', 'negative', 'average'], ['NULL', 'food quality', 'negative', 'shredded'], ['NULL', 'food quality', 'negative', 'no seasoning']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Nice Family owned traditional restaurant .']", "output": "[['restaurant', 'restaurant general', 'positive', 'traditional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['An awesome organic dog , and a conscious eco friendly establishment .']", "output": "[['dog', 'food quality', 'positive', 'organic'], ['establishment', 'restaurant miscellaneous', 'positive', 'eco friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend this beautiful place .']", "output": "[['place', 'ambience general', 'positive', 'recommend'], ['place', 'restaurant general', 'positive', 'beautiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Less than three minutes passed before I found myself doubled over the toilet .']", "output": "[['NULL', 'food quality', 'negative', 'doubled over the toilet']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not much of a selection of bottled beer either , we went with Brahma .']", "output": "[['selection of bottled beer', 'drinks style_options', 'negative', 'Not much']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"however , it 's the service that leaves a bad taste in my mouth .\"]", "output": "[['service', 'service general', 'negative', 'bad taste']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They should have called it mascarpone with chocolate chips-good but a far cry from what the name implies .']", "output": "[['NULL', 'food quality', 'negative', 'a far cry']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place has the best Chinese style BBQ ribs in the city .']", "output": "[['BBQ ribs', 'food quality', 'positive', 'best'], ['BBQ ribs', 'food style_options', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The best !']", "output": "[['NULL', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place survives on reputation alone .']", "output": "[['place', 'restaurant miscellaneous', 'negative', 'survives on reputation alone']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The decor is night tho ... but they REALLY need to clean that vent in the ceiling ... its quite un-appetizing , and kills your effort to make this place look sleek and modern .']", "output": "[['ceiling', 'ambience general', 'negative', 'un-appetizing'], ['vent', 'ambience general', 'negative', 'un-appetizing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waiter was attentive , the food was delicious and the views of the city were great .']", "output": "[['waiter', 'service general', 'positive', 'attentive'], ['food', 'food quality', 'positive', 'delicious'], ['views of the city', 'location general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ambience is delightful , service impeccable .']", "output": "[['Ambience', 'ambience general', 'positive', 'delightful'], ['service', 'service general', 'positive', 'impeccable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only thing that strikes you is the decor ? ( not very pleasant ) .']", "output": "[['decor', 'ambience general', 'negative', 'not very pleasant']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Delicate spices , onions , eggs and a kick-ass roti .']", "output": "[['spices', 'food quality', 'positive', 'Delicate'], ['onions', 'food quality', 'positive', 'Delicate'], ['eggs', 'food quality', 'positive', 'Delicate'], ['roti', 'food quality', 'positive', 'kick-ass']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was delicious ( I had a halibut special , my husband had steak ) , and the service was top-notch .']", "output": "[['food', 'food quality', 'positive', 'delicious'], ['halibut special', 'food quality', 'positive', 'delicious'], ['steak', 'food quality', 'positive', 'delicious'], ['service', 'service general', 'positive', 'top-notch']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\"]", "output": "[['wait staff', 'service general', 'positive', 'freindly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Big thumbs up !']", "output": "[['NULL', 'restaurant general', 'positive', 'thumbs up']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I have been to Rao 's probably 15 times the past 3 years and it keeps getting better .\"]", "output": "[[\"Rao 's\", 'restaurant general', 'positive', 'better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Bukhara Grill , the tagline says it all . . `` INDIAN SPICE RAVE ``']", "output": "[['Bukhara Grill', 'restaurant general', 'positive', 'RAVE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['MY husbands birthday and my sons was not as it was intended ... and we drove two hours to spend too much money to be treated terribly !']", "output": "[['NULL', 'restaurant prices', 'negative', 'too much money'], ['NULL', 'service general', 'negative', 'treated terribly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The atmosphere was pretty nice but had a bit lacking , which it tries to make up for with a crazy scheme of mirrors .']", "output": "[['atmosphere', 'ambience general', 'negative', 'nice'], ['scheme of mirrors', 'ambience general', 'negative', 'crazy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .']", "output": "[['NULL', 'food style_options', 'negative', 'served with skin'], ['spinach', 'food quality', 'negative', 'undercooked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place has totally weird decor , stairs going up with mirrored walls - I am surprised how no one yet broke their head or fall off the stairs - mirrored walls make you dizzy and delusional ...']", "output": "[['decor', 'ambience general', 'negative', 'weird'], ['mirrored walls', 'ambience general', 'negative', 'dizzy'], ['mirrored walls', 'ambience general', 'negative', 'delusional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is amazing , rich pastas and fresh doughy pizza .']", "output": "[['food', 'food quality', 'positive', 'amazing'], ['pastas', 'food style_options', 'positive', 'rich'], ['pizza', 'food quality', 'positive', 'fresh doughy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['limited menu , no-so-fresh ingredients , thinly-sliced fish , fall-apart rice .']", "output": "[['menu', 'food style_options', 'negative', 'limited'], ['ingredients', 'food quality', 'negative', 'no-so-fresh'], ['fish', 'food style_options', 'negative', 'thinly-sliced'], ['rice', 'food style_options', 'negative', 'fall-apart']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I like the somosas , chai , and the chole , but the dhosas and dhal were kinda dissapointing .']", "output": "[['somosas', 'food quality', 'positive', 'like'], ['chai', 'food quality', 'positive', 'like'], ['chole', 'food quality', 'positive', 'like'], ['dhosas', 'food quality', 'negative', 'dissapointing'], ['dhal', 'food quality', 'negative', 'dissapointing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food is great .']", "output": "[['Food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The hot dogs are good , yes , but the reason to get over here is the fantastic pork croquette sandwich , perfect on its supermarket squishy bun .']", "output": "[['hot dogs', 'food quality', 'positive', 'good'], ['pork croquette sandwich', 'food quality', 'positive', 'fantastic'], ['bun', 'food quality', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend it .']", "output": "[['NULL', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great Indian Food !']", "output": "[['Indian Food', 'food quality', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Always good drinks and service is pretty good ;']", "output": "[['drinks', 'drinks quality', 'positive', 'good'], ['service', 'service general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Highly recommend it !']", "output": "[['NULL', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !']", "output": "[['NULL', 'service general', 'negative', 'not helpful in the least']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The prices were CHEAP compared to the quality of service and food .']", "output": "[['NULL', 'restaurant prices', 'positive', 'CHEAP']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['it is not consistent .']", "output": "[['NULL', 'restaurant general', 'negative', 'not consistent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\"]", "output": "[['food', 'food quality', 'positive', 'amazing'], ['selection of wines', 'drinks style_options', 'positive', 'extensive'], [\"Chef 's tasting menu\", 'food quality', 'positive', 'favourite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The worst excuse for Japanese food I 've ever encountered .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'worst']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I plan on stopping by next week as well .']", "output": "[['NULL', 'restaurant general', 'positive', 'stopping by next week']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['supercilious scorn is in .']", "output": "[['NULL', 'service general', 'negative', 'supercilious scorn']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Someone else recommended the dessert - we also left that .']", "output": "[['dessert', 'food quality', 'negative', 'recommended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is the perfect date spot for Williamsburg couples .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Indoor was very cozy and cute .']", "output": "[['Indoor', 'ambience general', 'positive', 'cozy'], ['Indoor', 'ambience general', 'positive', 'cute']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only beverage we did receive was water in dirty glasses !']", "output": "[['NULL', 'service general', 'negative', 'dirty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's one of our favorite places to eat in NY .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Salads are a delicious way to begin the meal .']", "output": "[['Salads', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['wont come back again for sure !']", "output": "[['NULL', 'restaurant general', 'negative', 'wont come back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place has ruined me for neighborhood sushi .']", "output": "[['sushi', 'food quality', 'positive', 'ruined']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Me and my girls will definitely go back .']", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The large selection of bruschettas , paninis , tramezzinis keep the palate from stagnating .']", "output": "[['bruschettas', 'food style_options', 'positive', 'large selection'], ['paninis', 'food style_options', 'positive', 'large selection'], ['tramezzinis', 'food style_options', 'positive', 'large selection']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I HAVE NEVER EVER HAD SUCH AN UNPLEASANT EXPERIENCE .']", "output": "[['NULL', 'restaurant general', 'negative', 'UNPLEASANT']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .']", "output": "[['grilled branzino', 'food quality', 'negative', 'difficult to eat']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The veal and the mushrooms were cooked perfectly .']", "output": "[['veal', 'food quality', 'positive', 'perfectly'], ['mushrooms', 'food quality', 'positive', 'perfectly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My friend devoured her chicken and mashed potatos .']", "output": "[['chicken and mashed potatos', 'food quality', 'positive', 'devoured']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is all shared so we get to order together and eat together .']", "output": "[['food', 'food style_options', 'positive', 'shared']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['And really large portions .']", "output": "[['portions', 'food style_options', 'positive', 'large']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not cheap but very yummy .']", "output": "[['NULL', 'food prices', 'negative', 'Not cheap'], ['NULL', 'food quality', 'positive', 'yummy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wish NY had more of these kind of places : intimate , superb food , homey , top notch all the way around , certainly worth the wait .']", "output": "[['NULL', 'ambience general', 'positive', 'intimate'], ['food', 'food quality', 'positive', 'superb'], ['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .']", "output": "[['staff', 'service general', 'negative', 'good'], ['Cafe Noir', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Seating is always prompt , though the restaurant does fill up in the evening .']", "output": "[['Seating', 'service general', 'positive', 'prompt']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The DJ is awesome , I have been there for my birthday and a bunch of other times with friends and I keep going back .']", "output": "[['DJ', 'ambience general', 'positive', 'awesome'], ['NULL', 'restaurant general', 'positive', 'keep going back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .']", "output": "[['anti-pasta', 'food quality', 'positive', 'excellent'], ['calamari', 'food quality', 'positive', 'excellent'], ['pasta mains', 'food quality', 'positive', 'excellent'], ['pasta mains', 'food style_options', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A great choice at any cost and a great deal .']", "output": "[['NULL', 'restaurant general', 'positive', 'A great choice'], ['NULL', 'restaurant prices', 'positive', 'a great deal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['love the food .']", "output": "[['food', 'food quality', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .']", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'start a new thread so they can stock it with positive reviews']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['She just nodded and walked off .']", "output": "[['NULL', 'service general', 'negative', 'walked off']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not only is the cuisine the best around , the service has always been attentive and charming .']", "output": "[['cuisine', 'food quality', 'positive', 'best'], ['service', 'service general', 'positive', 'attentive'], ['service', 'service general', 'positive', 'charming']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['too large for just two people but nothing was left .']", "output": "[['NULL', 'food style_options', 'negative', 'too large'], ['NULL', 'food quality', 'positive', 'nothing was left']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"We didn 't look like the other patrons in there so unfortunately I think that may have been part of the problem .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'unfortunately']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If you are the type of person who likes being scared and entertained , this is a great place to go and eat .']", "output": "[['place', 'restaurant miscellaneous', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The flavors are amazing and the value is phenomenal .']", "output": "[['NULL', 'food quality', 'positive', 'amazing'], ['NULL', 'food prices', 'positive', 'phenomenal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ambiance relaxed and stylish .']", "output": "[['Ambiance', 'ambience general', 'positive', 'relaxed'], ['Ambiance', 'ambience general', 'positive', 'stylish']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend visiting this restaurant and having dinner and drinks !']", "output": "[['restaurant', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had a huge group for my birthday and we were well taken care of .']", "output": "[['NULL', 'service general', 'positive', 'well taken care of']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Just go to Yamato and order the Red Dragon Roll .']", "output": "[['Yamato', 'restaurant general', 'positive', 'Just go']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The pasta was well cooked , did n't have enough sauce though or flavor .\"]", "output": "[['pasta', 'food quality', 'positive', 'well cooked'], ['pasta', 'food quality', 'negative', \"did n't have enough sauce\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine the service was very good too .']", "output": "[['wine', 'drinks quality', 'positive', 'good'], ['service', 'service general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Suan is a great place that I often take my friends ( classmates ) too .']", "output": "[['Suan', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['By far the best salad I have had in a fast food restaurant .']", "output": "[['salad', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would highly recommend it .']", "output": "[['NULL', 'restaurant general', 'positive', 'highly recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Way below average']", "output": "[['NULL', 'restaurant general', 'negative', 'below average']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ambience is so cute and quaint , good for business although we were there on vacation .']", "output": "[['Ambience', 'ambience general', 'positive', 'cute'], ['Ambience', 'ambience general', 'positive', 'quaint'], ['Ambience', 'ambience general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food is great and inexpensive .']", "output": "[['Food', 'food quality', 'positive', 'great'], ['Food', 'food prices', 'positive', 'inexpensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Awsome Pizza especially the Margheritta slice .']", "output": "[['Pizza', 'food quality', 'positive', 'Awsome'], ['Margheritta slice', 'food quality', 'positive', 'Awsome']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['( The asparagus , truffle oil , parmesan bruschetta is a winner ! )']", "output": "[['asparagus , truffle oil , parmesan bruschetta', 'food quality', 'positive', 'winner']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Considering you will spend at least $ 60 a head , I expect better service .']", "output": "[['service', 'service general', 'negative', 'expect better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If you want good authentic Thai this place is not the place to go .']", "output": "[['Thai', 'food quality', 'negative', 'good authentic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['During the course of the past 3 months , the chef and staff changed and it was not for the better .']", "output": "[['chef', 'food quality', 'negative', 'changed'], ['staff', 'service general', 'negative', 'changed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .']", "output": "[['congee', 'food quality', 'positive', 'Try'], ['Ow Ley Soh', 'food quality', 'positive', 'delicious'], ['Ow Ley Soh', 'food quality', 'positive', 'sweet']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We could have made a meal of the yummy dumplings from the dumpling menu .']", "output": "[['dumplings', 'food quality', 'positive', 'yummy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The setting is casual and romantic .']", "output": "[['setting', 'ambience general', 'positive', 'casual'], ['setting', 'ambience general', 'positive', 'romantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sushi seemed pretty fresh and was adequately proportioned .']", "output": "[['sushi', 'food quality', 'positive', 'fresh'], ['sushi', 'food style_options', 'positive', 'proportioned']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A pleasant surprise .']", "output": "[['NULL', 'restaurant general', 'positive', 'pleasant surprise']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great neighborhood joint .']", "output": "[['joint', 'restaurant general', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Food was amazing - I love Indian food and eat it quite regularly , but I can say this is one of the best I 've had .\"]", "output": "[['Food', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Though it 's been crowded most times I 've gone here , Bark always delivers on their food .\"]", "output": "[['food', 'food quality', 'positive', 'delivers on their food'], ['Bark', 'restaurant miscellaneous', 'neutral', 'crowded']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It was n't as if this restaurant had any major bragging points before hand , but now it 's simply repulsive .\"]", "output": "[['restaurant', 'restaurant general', 'negative', 'repulsive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the bestt !']", "output": "[['NULL', 'restaurant general', 'positive', 'bestt']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Look , the appetizers were really good .']", "output": "[['appetizers', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Spreads and toppings are great - though a bit pricey .']", "output": "[['Spreads', 'food quality', 'positive', 'great'], ['toppings', 'food quality', 'positive', 'great'], ['Spreads', 'food prices', 'negative', 'pricey'], ['toppings', 'food prices', 'negative', 'pricey']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'm still mad that i had to pay for lousy food .\"]", "output": "[['food', 'food quality', 'negative', 'lousy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Right off the L in Brooklyn this is a nice cozy place with good pizza .']", "output": "[['pizza', 'food quality', 'positive', 'good'], ['place', 'ambience general', 'positive', 'nice cozy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A beautifully designed dreamy Egyptian restaurant that gets sceney at night .']", "output": "[['Egyptian restaurant', 'ambience general', 'positive', 'dreamy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Cheese plate is a varied delight and great bargain at $ 10 .']", "output": "[['Cheese plate', 'food quality', 'positive', 'varied delight'], ['Cheese plate', 'food style_options', 'positive', 'varied delight'], ['Cheese plate', 'food prices', 'positive', 'great bargain']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was there for brunch recently , and we were tag teamed by a waitress and a waiter .']", "output": "[['waitress', 'service general', 'negative', 'tag teamed'], ['waiter', 'service general', 'negative', 'tag teamed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Impressed ...']", "output": "[['NULL', 'restaurant general', 'positive', 'Impressed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"As BFC does n't take reservations you almost always have to wait by the bar - and be abused by the front of house staff until you are seated , which can be over an hour later !\"]", "output": "[['BFC', 'restaurant miscellaneous', 'negative', 'wait'], ['front of house staff', 'service general', 'negative', 'abused'], ['NULL', 'service general', 'negative', 'over an hour later']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['And it was quick which is very important .']", "output": "[['NULL', 'service general', 'positive', 'quick']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Stay away']", "output": "[['NULL', 'restaurant general', 'negative', 'Stay away']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The flavors robust and subtle .']", "output": "[['NULL', 'food quality', 'positive', 'robust'], ['NULL', 'food quality', 'positive', 'subtle']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Just not good at all .']", "output": "[['NULL', 'food quality', 'negative', 'not good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great spot , whether looking for a couple of drinks or quiet dinner .']", "output": "[['spot', 'restaurant general', 'positive', 'Great'], ['spot', 'restaurant miscellaneous', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The drinks are great , especially when made by Raymond .']", "output": "[['drinks', 'drinks quality', 'positive', 'great'], ['Raymond', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The dishes offered were unique , very tasty and fresh from the lamb sausages , sardines with biscuits , large whole shrimp to the amazing pistachio ice cream ( the best and freshest I 've ever had ) .\"]", "output": "[['dishes', 'food quality', 'positive', 'unique'], ['dishes', 'food quality', 'positive', 'tasty'], ['dishes', 'food quality', 'positive', 'fresh'], ['pistachio ice cream', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['After all that , they complained to me about the small tip .']", "output": "[['NULL', 'service general', 'negative', 'complained']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Give it a try and enjoy .']", "output": "[['NULL', 'restaurant general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"When the bill came , nothing was comped , so I told the manager very politely that we were willing to pay for the wine , but I didn 't think I should have to pay for food with a maggot in it .\"]", "output": "[['NULL', 'service general', 'negative', 'maggot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is great and they have a good selection of wines at reasonable prices .']", "output": "[['food', 'food quality', 'positive', 'great'], ['wines', 'drinks style_options', 'positive', 'good selection'], ['wines', 'drinks prices', 'positive', 'good selection']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's a little out of our price range for dining there except on special occasions , but we 've eaten there 6 times in the last 2 years .\"]", "output": "[['NULL', 'restaurant prices', 'negative', 'out of our price range']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Thank You Emilio .']", "output": "[['Emilio', 'restaurant general', 'positive', 'Thank You']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waitress was very patient with us and the food is phenomenal !']", "output": "[['waitress', 'service general', 'positive', 'patient'], ['food', 'food quality', 'positive', 'phenomenal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Surprisingly nothing could be further from the truth .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'be further from the truth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great pizza for lunch place .']", "output": "[['pizza', 'food quality', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['food was luke warm .']", "output": "[['food', 'food quality', 'negative', 'luke warm']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food they serve is not comforting , not appetizing and uncooked .']", "output": "[['food', 'food quality', 'negative', 'not comforting'], ['food', 'food quality', 'negative', 'not appetizing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The entertainment was great they have shows that go on through out the dinner .']", "output": "[['entertainment', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Check out the secret back room .']", "output": "[['back room', 'ambience general', 'positive', 'secret']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lox is always fresh too .']", "output": "[['lox', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is a great place to take out-of-towners , and perfect for watching the sunset .']", "output": "[['place', 'location general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I expected quite a bit more from such an expensive menu .']", "output": "[['menu', 'food prices', 'negative', 'expensive'], ['menu', 'food quality', 'negative', 'expensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the salads are delicious , both refreshing and very spicy .']", "output": "[['salads', 'food quality', 'positive', 'delicious'], ['salads', 'food quality', 'positive', 'refreshing'], ['salads', 'food quality', 'positive', 'spicy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Also very inexpensive .']", "output": "[['NULL', 'restaurant prices', 'positive', 'inexpensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place was quiet and delightful .']", "output": "[['place', 'ambience general', 'positive', 'quiet'], ['place', 'ambience general', 'positive', 'delightful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good Experience']", "output": "[['NULL', 'restaurant general', 'positive', 'Good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Still , any quibbles about the bill were off-set by the pour-your-own measures of liquers which were courtesey of the house ...']", "output": "[['NULL', 'restaurant prices', 'neutral', 'quibbles'], ['measures of liquers', 'drinks style_options', 'positive', 'pour-your-own'], ['measures of liquers', 'drinks style_options', 'positive', 'courtesey']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I thought I had died and gone to heaven .']", "output": "[['NULL', 'food quality', 'positive', 'heaven']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Indo Chinese food , pretty good ...']", "output": "[['Indo Chinese food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['even the wine by the glass was good .']", "output": "[['wine by the glass', 'drinks quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place has a nice fit-out , some attractive furnishings and , from what I could tell , a reasonable wine list ( I was given the food menu when I asked for the carte des vins )']", "output": "[['fit-out', 'ambience general', 'positive', 'nice'], ['furnishings', 'ambience general', 'positive', 'attractive'], ['wine list', 'drinks style_options', 'positive', 'reasonable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place has got to be the best japanese restaurant in the new york area .']", "output": "[['place', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Rao is a good restaurant , but it 's nothing special .\"]", "output": "[['Rao', 'restaurant general', 'neutral', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only fallback on this restaurant is the prices .']", "output": "[['restaurant', 'restaurant prices', 'negative', 'fallback']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The portions are small but being that the food was so good makes up for that .']", "output": "[['portions', 'food style_options', 'negative', 'small'], ['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only thing that my friend left out is that when we sat down at the bar the bartender disappeared .']", "output": "[['bartender', 'service general', 'negative', 'disappeared']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Priced at upper intermediate range .']", "output": "[['NULL', 'restaurant prices', 'negative', 'upper intermediate']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is so cheap and the waiters are nice .']", "output": "[['food', 'food prices', 'positive', 'cheap'], ['waiters', 'service general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .']", "output": "[['food', 'food quality', 'neutral', 'decent'], ['food', 'food quality', 'negative', 'not great'], ['lemon salad', 'food quality', 'negative', 'exception']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was extremely fast and attentive ( thanks to the service button on your table ) but I barely understood 1 word when the waiter took our order .']", "output": "[['service', 'service general', 'positive', 'fast'], ['service', 'service general', 'positive', 'attentive'], ['service button', 'service general', 'positive', 'thanks to'], ['waiter', 'service general', 'negative', 'barely understood']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My quesadilla tasted like it had been made by a three-year old with no sense of proportion or flavor .']", "output": "[['quesadilla', 'food quality', 'negative', 'no sense of proportion or flavor'], ['quesadilla', 'food style_options', 'negative', 'no sense of proportion']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The boths are not as small as some of the reviews make them out to look they 're perfect for 2 people .\"]", "output": "[['boths', 'ambience general', 'positive', 'not as small']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Even my Indian friend couldn 't believe how good and tasty everything was .\"]", "output": "[['NULL', 'food quality', 'positive', 'good'], ['NULL', 'food quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\"]", "output": "[['waiter', 'service general', 'negative', 'snobby']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've never had bad service and the fish is fresh and delicious .\"]", "output": "[['service', 'service general', 'positive', 'never had bad'], ['fish', 'food quality', 'positive', 'fresh'], ['fish', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"There 's another girl who I ca n't describe , she is about 5 ' 6 `` with brown hair , who eavesdrops on your conversation and chimes in - except she only hears the last part of what you said , so her uninvited opinions are often out of context and nothing to do with what you 're *really * talking about .\"]", "output": "[['girl', 'service general', 'negative', 'uninvited']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Fabulous decor - makes you feel like you 're in a trendy Manhattan restaurant , very very good food , cheaply-priced , generally friendly staff , and if you 're a Manhattanite , or spend most of your time in Manhattan , Rice Avenue will make you feel at home ... ..very Soho /Village /Upper West Side minus the expensive prices and pretentious clientele ... ..all on Roosevelt Avenue !\"]", "output": "[['decor', 'ambience general', 'positive', 'Fabulous'], ['food', 'food quality', 'positive', 'good'], ['food', 'food prices', 'positive', 'cheaply-priced'], ['staff', 'service general', 'positive', 'friendly'], ['Rice Avenue', 'ambience general', 'positive', 'feel at home'], ['Rice Avenue', 'location general', 'positive', 'very Soho /Village /Upper West Side'], ['Rice Avenue', 'restaurant prices', 'positive', 'minus the expensive prices']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food , great decor , great service .']", "output": "[['food', 'food quality', 'positive', 'Great'], ['decor', 'ambience general', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"They do n't concern much of customer 's health , just want to make money .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', \"do n't concern much\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['THIS STAFF SHOULD BE FIRED .']", "output": "[['STAFF', 'service general', 'negative', 'FIRED']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We will definitely go back .']", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When I lived upstate for a while I would buy freeze the bagels and they would still be better than any else .']", "output": "[['bagels', 'food quality', 'positive', 'better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Excellent dumplings served amid clean , chic decor .']", "output": "[['dumplings', 'food quality', 'positive', 'Excellent'], ['decor', 'ambience general', 'positive', 'clean'], ['decor', 'ambience general', 'positive', 'chic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Their bagels are fine , but they are a little overcooked , and not really a 'special ' bagel experience .\"]", "output": "[['bagels', 'food quality', 'negative', 'fine'], ['bagels', 'food quality', 'negative', 'overcooked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ravioli was good ... but I have to say that I found everything a bit overpriced .']", "output": "[['Ravioli', 'food quality', 'positive', 'good'], ['NULL', 'food prices', 'negative', 'overpriced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I think the pizza is so overrated and was under cooked .']", "output": "[['pizza', 'food quality', 'negative', 'overrated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Went on a 3 day oyster binge , with Fish bringing up the closing , and I am so glad this was the place it O trip ended , because it was so great !']", "output": "[['oyster binge', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The place is a bit hidden away , but once you get there , it 's all worth it .\"]", "output": "[['place', 'location general', 'neutral', 'hidden away'], ['place', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !']", "output": "[['spot', 'restaurant miscellaneous', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I came across Village Underground by accident , now I go there all the time .']", "output": "[['Village Underground', 'restaurant general', 'positive', 'go there all the time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Rude service , medicore food ... there are tons of restaurants in NY ... stay away from this one']", "output": "[['service', 'service general', 'negative', 'Rude'], ['food', 'food quality', 'neutral', 'medicore'], ['NULL', 'restaurant general', 'negative', 'stay away']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I took one bite from the $ 24 salmon , and I have never , in the 17 years I have been going to restaurants tasted salmon as fishy , as dry , and as bland as the one in Flatbush Farms .']", "output": "[['salmon', 'food quality', 'negative', 'fishy'], ['salmon', 'food quality', 'negative', 'dry'], ['salmon', 'food quality', 'negative', 'bland'], ['salmon', 'food prices', 'negative', '$ 24']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I felt as though I were eating in Paris .']", "output": "[['NULL', 'food quality', 'positive', 'eating in Paris']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My husbands was perfect , my was well done and dry .']", "output": "[['NULL', 'food quality', 'positive', 'perfect'], ['NULL', 'food quality', 'negative', 'well done'], ['NULL', 'food quality', 'negative', 'dry']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not impressed with the food .']", "output": "[['food', 'food quality', 'negative', 'Not impressed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The pasta penne was pretty extra buttery , creamy which means a big task to diggest . . tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne ... got a little moody afterwards cause was stuffed ... lol']", "output": "[['pasta penne', 'food quality', 'negative', 'extra buttery'], ['pasta penne', 'food style_options', 'negative', 'moody']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['great food']", "output": "[['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Saul is the best restaurant on Smith Street and in Brooklyn .']", "output": "[['Saul', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['First went here to enjoy their garden terrace .']", "output": "[['garden terrace', 'ambience general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I / we will never go back to this place again .']", "output": "[['place', 'restaurant general', 'negative', 'never go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['sometimes i get good food and ok service .']", "output": "[['food', 'food quality', 'positive', 'good'], ['service', 'service general', 'neutral', 'ok']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Again , I 'd be super upset if that were my employee .\"]", "output": "[['NULL', 'service general', 'negative', 'upset']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everything was wonderful ; food , drinks , staff , mileau .']", "output": "[['food', 'food quality', 'positive', 'wonderful'], ['drinks', 'drinks quality', 'positive', 'wonderful'], ['staff', 'service general', 'positive', 'wonderful'], ['mileau', 'ambience general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['For a Fabulous Wedding !']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'Fabulous']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everything is always cooked to perfection , the service is excellent , the decor cool and understated .']", "output": "[['NULL', 'food quality', 'positive', 'perfection'], ['service', 'service general', 'positive', 'excellent'], ['decor', 'ambience general', 'positive', 'cool'], ['decor', 'ambience general', 'positive', 'understated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I have been going there since it opened and I ca n't get enough .\"]", "output": "[['NULL', 'restaurant general', 'positive', \"ca n't get enough\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['delicious simple food in nice outdoor atmosphere .']", "output": "[['food', 'food quality', 'positive', 'delicious simple'], ['food', 'food style_options', 'positive', 'delicious simple'], ['outdoor atmosphere', 'ambience general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We thought that this place is using too much of MSG cooking in the foods .']", "output": "[['foods', 'food quality', 'negative', 'using too much of MSG']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everyone was smiling so that made me feel welcome .']", "output": "[['NULL', 'service general', 'positive', 'welcome']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Guaranteed to be a tasty experience ! )']", "output": "[['NULL', 'drinks quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['As a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .']", "output": "[['Lucky Strike', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Highly recommended !']", "output": "[['NULL', 'restaurant general', 'positive', 'Highly recommended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The service varys from day to day- sometimes they 're very nice , and sometimes not .\"]", "output": "[['service', 'service general', 'negative', 'varys']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .']", "output": "[['ambience', 'ambience general', 'positive', 'correct'], ['staff', 'service general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was bland oily .']", "output": "[['food', 'food quality', 'negative', 'bland oily']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Simply some good tasting Chinese food at incredible prices ...']", "output": "[['Chinese food', 'food quality', 'positive', 'good tasting'], ['Chinese food', 'food prices', 'positive', 'good tasting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My mom originally introduced me to this place , but even she ( being Indian ) feels the food can be somewhat over the top spicy and far too oily .']", "output": "[['food', 'food quality', 'negative', 'spicy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great service , great food .']", "output": "[['service', 'service general', 'positive', 'Great'], ['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Warm and friendly in the winter and terrific outdoor seating in the warmer months .']", "output": "[['NULL', 'ambience general', 'positive', 'Warm'], ['NULL', 'ambience general', 'positive', 'friendly'], ['outdoor seating', 'ambience general', 'positive', 'terrific']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service friendly and attentive .']", "output": "[['Service', 'service general', 'positive', 'friendly'], ['Service', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\"]", "output": "[['food', 'food quality', 'positive', 'above average'], ['Teodora', 'restaurant general', 'negative', 'deficiencies']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food arrived 20 minutes after I called , cold and soggy .']", "output": "[['food', 'food quality', 'negative', 'cold'], ['food', 'food quality', 'negative', 'soggy'], ['NULL', 'service general', 'negative', '20 minutes']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['highly recommended .']", "output": "[['NULL', 'restaurant general', 'positive', 'highly recommended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff was the friendliest that have seen in New York .']", "output": "[['staff', 'service general', 'positive', 'friendliest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The fish was not fresh and the rice tasted old and stale .']", "output": "[['fish', 'food quality', 'negative', 'not fresh'], ['rice', 'food quality', 'negative', 'old'], ['rice', 'food quality', 'negative', 'stale']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great Hot Dogs !']", "output": "[['Hot Dogs', 'food quality', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food was okay , nothing great .']", "output": "[['Food', 'food quality', 'neutral', 'okay'], ['Food', 'food quality', 'neutral', 'nothing great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .']", "output": "[['NULL', 'food quality', 'positive', 'delicious'], ['NULL', 'ambience general', 'positive', 'perfect quiet'], ['NULL', 'ambience general', 'positive', 'relaxing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Even though the place is not beautiful , the food speaks for itself .']", "output": "[['place', 'ambience general', 'negative', 'not beautiful'], ['food', 'food quality', 'positive', 'speaks for itself']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My wife and I ate here earlier this week and have not stopped ranting and raving about the food .']", "output": "[['food', 'food quality', 'positive', 'raving']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine list is also really nice .']", "output": "[['wine list', 'drinks style_options', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is a wonderful place on all stand points especially value ofr money .']", "output": "[['place', 'restaurant prices', 'positive', 'wonderful'], ['place', 'restaurant general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great wine selection , Gigondas is worth the price , and the house champagne is a great value .']", "output": "[['wine selection', 'drinks style_options', 'positive', 'Great'], ['Gigondas', 'drinks quality', 'positive', 'worth the price'], ['house champagne', 'drinks prices', 'positive', 'great value']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was spectacular as the waiter knew everything about the menu and his recommendations were amazing !']", "output": "[['service', 'service general', 'positive', 'spectacular'], ['waiter', 'service general', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Very cozy and warm inside ...']", "output": "[['NULL', 'ambience general', 'positive', 'cozy'], ['NULL', 'ambience general', 'positive', 'warm']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .']", "output": "[['service', 'service general', 'positive', 'good'], ['staff', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"it 's delicious !\"]", "output": "[['NULL', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Excellent food for great prices']", "output": "[['food', 'food quality', 'positive', 'Excellent'], ['food', 'food prices', 'positive', 'Excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food tasted very good .']", "output": "[['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wait staff is very courteous and accomodating .']", "output": "[['wait staff', 'service general', 'positive', 'courteous']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is so much fun .']", "output": "[['place', 'ambience general', 'positive', 'fun']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\"]", "output": "[['food', 'food prices', 'negative', 'overpriced'], ['wait-staff', 'service general', 'negative', 'arrogant'], ['service', 'service general', 'negative', 'clumsy'], ['management', 'service general', 'negative', \"does n't care\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['So , I switch with my boyfriend again to see if maybe I could stomach the meat and spinach again , but the spinach was so undercooked that I just could not bite through it .']", "output": "[['spinach', 'food quality', 'negative', 'undercooked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It was $ 14 not really bad for a pound of Pastrami-but it did n't have much taste-I 've had better for less elsewhere !\"]", "output": "[['NULL', 'food prices', 'neutral', 'not really bad'], ['NULL', 'food quality', 'negative', \"did n't have much taste\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not the typical NYC gimmick theme restaurant .']", "output": "[['restaurant', 'ambience general', 'positive', 'Not the typical']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .']", "output": "[['hot dogs', 'food quality', 'positive', 'juicy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['After really enjoying ourselves at the bar we sat down at a table and had dinner .']", "output": "[['bar', 'restaurant miscellaneous', 'positive', 'enjoying']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Also , the sandwiches ( nearing $ 7 ) did n't come with anything like chips or a side .\"]", "output": "[['sandwiches', 'food style_options', 'negative', \"did n't come with\"], ['sandwiches', 'food prices', 'negative', 'nearing $ 7 ']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Reliable , Fresh Sushi']", "output": "[['Sushi', 'food quality', 'positive', 'Reliable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !']", "output": "[['atmosphere', 'ambience general', 'positive', 'raved'], ['rooms', 'ambience general', 'positive', 'elegant'], ['views', 'location general', 'positive', 'incomparable'], ['food', 'food quality', 'positive', 'fabulous']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"A restaurant that does n't try to do anything except serve great food with great service in a pleasant atmosphere .\"]", "output": "[['food', 'food quality', 'positive', 'great'], ['service', 'service general', 'positive', 'great'], ['atmosphere', 'ambience general', 'positive', 'pleasant']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I recently went to this restaurant with some co-workers for lunch and had an amazing time .']", "output": "[['restaurant', 'restaurant general', 'positive', 'amazing time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Atmosphere is nice and relaxed too ...']", "output": "[['Atmosphere', 'ambience general', 'positive', 'nice'], ['Atmosphere', 'ambience general', 'positive', 'relaxed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best Reuben sandwich ever !']", "output": "[['Reuben sandwich', 'food quality', 'positive', 'Best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was friendly and the atmosphere was casual .']", "output": "[['service', 'service general', 'positive', 'friendly'], ['atmosphere', 'ambience general', 'neutral', 'casual']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Paul , the maitre d ' , was totally professional and always on top of things .\"]", "output": "[['Paul', 'service general', 'positive', 'professional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Overall the food quality was pretty good , though I hear the salmon is much better when it has n't sat cooling in front of the guest .\"]", "output": "[['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Tasty Dog !']", "output": "[['Dog', 'food quality', 'positive', 'Tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Problem is nothing at Prune is particularly memorable .']", "output": "[['Prune', 'restaurant general', 'negative', 'memorable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the turkey burgers are scary !']", "output": "[['turkey burgers', 'food quality', 'negative', 'scary']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .']", "output": "[['porcini mushroom pasta special', 'food quality', 'negative', 'tasteless'], ['seafood tagliatelle', 'food quality', 'negative', 'tasteless']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We took advanatage of the half price sushi deal on saturday so it was well worth it .']", "output": "[['half price sushi deal', 'food quality', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The restaurant looks out over beautiful green lawns to the Hudson River and the Statue of Liberty .']", "output": "[['restaurant', 'location general', 'positive', 'beautiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Even if the food was n't this good , the garden is a great place to sit outside and relax .\"]", "output": "[['garden', 'ambience general', 'positive', 'great'], ['food', 'food quality', 'positive', \"was n't this good\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The hostess was rude and I got a distinct feeling that they did not want to serve us .']", "output": "[['hostess', 'service general', 'negative', 'rude'], ['NULL', 'service general', 'negative', 'distinct']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"My daughter 's wedding reception at Water 's Edge received the highest compliments from our guests .\"]", "output": "[[\"Water 's Edge\", 'restaurant miscellaneous', 'positive', 'highest compliments']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Add to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .']", "output": "[['service', 'service general', 'positive', 'great'], ['food', 'food quality', 'positive', 'great'], ['food', 'food prices', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Delicious crab cakes too .']", "output": "[['crab cakes', 'food quality', 'positive', 'Delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We had a good time .']", "output": "[['NULL', 'restaurant general', 'positive', 'good time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is good , especially their more basic dishes , and the drinks are delicious .']", "output": "[['food', 'food quality', 'positive', 'good'], ['basic dishes', 'food quality', 'positive', 'good'], ['drinks', 'drinks quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was also horrible and the ambience is not that great .']", "output": "[['Service', 'service general', 'negative', 'horrible'], ['ambience', 'ambience general', 'negative', 'not that great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"But if you 're prepared to spend some $ and remember to ask if something they offer is complimentary , then this is the place to go for Indian food\"]", "output": "[['Indian food', 'food quality', 'positive', 'the place to go'], ['place', 'restaurant prices', 'positive', 'complimentary']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only problem is that the manager is a complete incompetent .']", "output": "[['manager', 'service general', 'negative', 'incompetent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Went here last night - nice decor , good service , but the food was surprisingly excellent .']", "output": "[['decor', 'ambience general', 'positive', 'nice'], ['service', 'service general', 'positive', 'good'], ['food', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"While finishing our meals which included a high-end bottle of wine , our son 's fiance joined us for a glass of wine and dessert .\"]", "output": "[['bottle of wine', 'drinks quality', 'positive', 'high-end']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I tried a couple other dishes but was n't too impressed .\"]", "output": "[['dishes', 'food quality', 'neutral', \"was n't too impressed\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great place to relax and enjoy your dinner']", "output": "[['place', 'ambience general', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['$ 170 down the toilet ...']", "output": "[['NULL', 'restaurant prices', 'negative', '$ 170']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['And amazingly cheap .']", "output": "[['NULL', 'food prices', 'positive', 'cheap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good creative rolls !']", "output": "[['rolls', 'food quality', 'positive', 'Good creative'], ['rolls', 'food style_options', 'positive', 'Good creative']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Delivery guy sometimes get upset if you do n't tip more than 10 % .\"]", "output": "[['Delivery guy', 'service general', 'negative', 'upset']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have been going back again and again .']", "output": "[['NULL', 'restaurant general', 'positive', 'going back again and again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .']", "output": "[['server', 'service general', 'negative', 'enhanced'], ['appetizer', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We liked it so much , that we will always make it a point to dine here when we visit New York .']", "output": "[['NULL', 'restaurant general', 'positive', 'liked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Most of the servers are very attentive , friendly and quite attractive .']", "output": "[['servers', 'service general', 'positive', 'attentive'], ['servers', 'service general', 'positive', 'friendly'], ['servers', 'service general', 'positive', 'attractive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"it 's the only place you can get yummy authentic japanese comfort food .\"]", "output": "[['japanese comfort food', 'food quality', 'positive', 'yummy authentic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .']", "output": "[['NULL', 'service general', 'negative', 'upset']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['As many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .']", "output": "[['NULL', 'service general', 'neutral', 'slow']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Yes , the prices are high , but I felt it was worth it .']", "output": "[['NULL', 'restaurant prices', 'negative', 'high'], ['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Love YUKA .']", "output": "[['YUKA', 'restaurant general', 'positive', 'Love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Over all the looks of the place exceeds the actual meals .']", "output": "[['looks', 'ambience general', 'positive', 'exceeds']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"You ca n't go wrong with this place .\"]", "output": "[['place', 'restaurant general', 'positive', \"ca n't go wrong\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"i 've been to sapphire twice and both times the food was fine , if not good .\"]", "output": "[['food', 'food quality', 'positive', 'fine']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They are extremely rude , not even apologizing for the horrible service we got and handing us a bill well over $ 500 for some drinks adn their pita bread !']", "output": "[['service', 'service general', 'negative', 'horrible'], ['drinks', 'drinks prices', 'negative', 'over $ 500'], ['pita bread', 'food prices', 'negative', 'over $ 500'], ['NULL', 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I loved everythig about it-especially the shows and actors .']", "output": "[['shows', 'ambience general', 'positive', 'loved'], ['actors', 'ambience general', 'positive', 'loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I highly recommend to anyone to give this place a try .']", "output": "[['place', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is a fun restaurant to go to .']", "output": "[['restaurant', 'restaurant general', 'positive', 'fun']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A guaranteeed delight !']", "output": "[['NULL', 'restaurant general', 'positive', 'delight']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were very pleasantly surprised .']", "output": "[['NULL', 'restaurant general', 'positive', 'pleasantly surprised']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Conveniently located too , being right on Bedford ave .']", "output": "[['NULL', 'location general', 'positive', 'Conveniently']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['However , it is jus too good to not praise it .']", "output": "[['NULL', 'restaurant general', 'positive', 'good'], ['NULL', 'restaurant general', 'positive', 'praise']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was excellent - friendly and attentive .']", "output": "[['service', 'service general', 'positive', 'excellent'], ['service', 'service general', 'positive', 'friendly'], ['service', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We have been to this place many times , and always have great food , wine , and service .']", "output": "[['food', 'food quality', 'positive', 'great'], ['wine', 'drinks quality', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .']", "output": "[['meals', 'food quality', 'negative', 'edible'], ['rosemary or orange flavoring', 'food quality', 'negative', 'weird']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is not inviting and the food is totally weird .']", "output": "[['place', 'ambience general', 'negative', 'not inviting'], ['food', 'food quality', 'negative', 'weird']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['HIGHLY RECOMMENDED ! ! ! ! !']", "output": "[['NULL', 'restaurant general', 'positive', 'RECOMMENDED']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Their tuna tartar appetizer is to die for .']", "output": "[['tuna tartar appetizer', 'food quality', 'positive', 'die for']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['keep up the good work .']", "output": "[['NULL', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not enough wines by the glass either .']", "output": "[['wines by the glass', 'drinks style_options', 'negative', 'Not enough']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The seats are uncomfortable if you are sitting against the wall on wooden benches .']", "output": "[['seats', 'ambience general', 'negative', 'uncomfortable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .']", "output": "[['ambience', 'ambience general', 'positive', 'pretty'], ['ambience', 'ambience general', 'positive', 'nice'], ['NULL', 'restaurant miscellaneous', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['So rushing us out was absolutely unnecessary !']", "output": "[['NULL', 'service general', 'negative', 'rushing us out']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['For desserts , we tried the frozen black sesame mousse ( interesting but not extraordinary ) and matcha ( powdered green tea ) and blueberry cheesecake , which was phenomenal .']", "output": "[['frozen black sesame mousse', 'food quality', 'neutral', 'interesting'], ['frozen black sesame mousse', 'food quality', 'neutral', 'not extraordinary'], ['matcha ( powdered green tea ) and blueberry cheesecake', 'food quality', 'positive', 'phenomenal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Worth the trip from Manhattan .']", "output": "[['NULL', 'restaurant general', 'positive', 'Worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The price very reasonable .']", "output": "[['NULL', 'restaurant prices', 'positive', 'reasonable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Drawbacks : service is slow and they do n't toast !\"]", "output": "[['service', 'service general', 'negative', 'slow']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They were very abrupt with me when I called and actually claimed the food was late because they were out of rice .']", "output": "[['NULL', 'service general', 'negative', 'abrupt']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I asked for an open faced cheese sandwich and the manager basically told me to take my business elsewhere !']", "output": "[['manager', 'service general', 'negative', 'take my business elsewhere']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"THE SERVICE IS AMAZING , i 've had different waiters and they were all nice , which is a rare thing in NYC .\"]", "output": "[['SERVICE', 'service general', 'positive', 'AMAZING'], ['waiters', 'service general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['So I decide to report back to the waitress because it was completely inedible .']", "output": "[['NULL', 'food quality', 'negative', 'inedible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would highly recommend .']", "output": "[['NULL', 'restaurant general', 'positive', 'highly recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was well prepared and the service impecable .']", "output": "[['food', 'food quality', 'positive', 'well prepared'], ['service', 'service general', 'positive', 'impecable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is outstanding and the service is quick , friendly and very professional .']", "output": "[['food', 'food quality', 'positive', 'outstanding'], ['service', 'service general', 'positive', 'quick'], ['service', 'service general', 'positive', 'friendly'], ['service', 'service general', 'positive', 'professional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They even scoop it out nice ( for those on a diet ) not too much not to little .']", "output": "[['NULL', 'food style_options', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best Indian Restaurant in the City']", "output": "[['Indian Restaurant', 'restaurant general', 'positive', 'Best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .']", "output": "[['caviar', 'food quality', 'positive', 'spectacular'], ['caviar', 'food quality', 'positive', 'enjoyed'], ['waitstaff', 'service general', 'positive', 'hospitable'], ['waitstaff', 'service general', 'positive', 'enjoyed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I agree that dining at Casa La Femme is like no other dining experience !']", "output": "[['Casa La Femme', 'restaurant general', 'negative', 'like no other dining experience']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best Indian Chinese in the city , by far !']", "output": "[['Indian Chinese', 'food quality', 'positive', 'Best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['There was a small wait , but shorter than I expected .']", "output": "[['wait', 'service general', 'positive', 'small'], ['wait', 'service general', 'positive', 'shorter']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"But after last night , Spice Grill is the only place I 'm eating indian cuisine .\"]", "output": "[['indian cuisine', 'food quality', 'positive', 'the only place']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\"]", "output": "[['food', 'food quality', 'negative', 'decent'], ['ambience', 'ambience general', 'negative', 'annoying']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Personally I like the margherita pizza better , but they are all good .']", "output": "[['margherita pizza', 'food quality', 'positive', 'like'], ['NULL', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If it seemed possible to do so while there I would have fought my bill since my dinner portion of my meal was inedible !']", "output": "[['meal', 'food quality', 'negative', 'inedible'], ['NULL', 'restaurant prices', 'negative', 'fought my bill']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"At first glance this place seems a bit pricey for a hot dog joint , but at Bark you do n't just get your average hot dog .\"]", "output": "[['Bark', 'restaurant prices', 'negative', 'pricey']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sashimi is always fresh and the rolls are innovative and delicious .']", "output": "[['sashimi', 'food quality', 'positive', 'fresh'], ['rolls', 'food style_options', 'positive', 'innovative'], ['rolls', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I ca n't wait to go back .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Delivery can be spot on or lacking depending on the weather and the day of the week .']", "output": "[['Delivery', 'service general', 'negative', 'lacking']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['There are no negatives to speak of .']", "output": "[['NULL', 'restaurant general', 'positive', 'no negatives']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The Four Seasons restaurant is a great experience .']", "output": "[['The Four Seasons restaurant', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Seriously , this place kicks ass .']", "output": "[['place', 'restaurant general', 'positive', 'kicks ass']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've enjoyed 99 % of the dishes we 've ordered with the only exceptions being the occasional too-authentic-for-me dish ( I 'm a daring eater but not THAT daring ) .\"]", "output": "[['dishes', 'food quality', 'positive', 'enjoyed'], ['dish', 'food quality', 'negative', 'too-authentic-for-me']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Ca n't wait to go back .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['amazing fresh dogs but best of all endless toppings ! ! !']", "output": "[['dogs', 'food quality', 'positive', 'amazing fresh'], ['toppings', 'food style_options', 'positive', 'best'], ['toppings', 'food style_options', 'positive', 'endless']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Love this place , every time we are in the city this is one of the places we always go .']", "output": "[['place', 'restaurant general', 'positive', 'Love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service is not what one would expect from a joint in this price category .']", "output": "[['Service', 'service general', 'negative', 'not what one would expect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I stumbled upon this great pizzeria as I explored my new neighborhood .']", "output": "[['pizzeria', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This quaint and romantic trattoria is at the top of my Manhattan restaurant list .']", "output": "[['trattoria', 'ambience general', 'positive', 'quaint'], ['trattoria', 'restaurant general', 'positive', 'romantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was delicious but do not come here on a empty stomach .']", "output": "[['food', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Patsy 's Pizza - true love\"]", "output": "[[\"Patsy 's Pizza\", 'restaurant general', 'positive', 'true love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Average to good Thai food , but terrible delivery .']", "output": "[['Thai food', 'food quality', 'positive', 'Average to good'], ['delivery', 'service general', 'negative', 'terrible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I heartily recommend .']", "output": "[['NULL', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"You can get a table without a reservation if you get there early I they do n't make you by bottles .\"]", "output": "[['NULL', 'service general', 'positive', 'get a table without a reservation']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You are not eating haut cuisine with subtle hints of whatever but : Cassuolet , Steake Fritte , Tripe Stew , etc ; simple stuff .']", "output": "[['NULL', 'food style_options', 'positive', 'simple'], ['NULL', 'food quality', 'positive', 'subtle hints of whatever']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['For the price , you can not eat this well in Manhattan .']", "output": "[['NULL', 'restaurant prices', 'negative', 'can not eat this well']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['One of the best , if not THE best , restaurants in Park Slope ( and NY in general )']", "output": "[['NULL', 'restaurant general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Always busy but fast moving .']", "output": "[['NULL', 'service general', 'positive', 'fast moving']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I tend to judge a sushi restaurant by its sea urchin , which was heavenly at sushi rose .']", "output": "[['sea urchin', 'food quality', 'positive', 'heavenly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Yakitori ( bbq meats ) is tasty too .']", "output": "[['Yakitori ( bbq meats )', 'food quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .']", "output": "[['fish', 'food quality', 'positive', 'quality'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Faan is sooo good .']", "output": "[['Faan', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Fresh , mind blowing flavors .']", "output": "[['NULL', 'food quality', 'positive', 'Fresh'], ['NULL', 'food quality', 'positive', 'mind blowing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Do n't judge this place prima facie , you have to try it to believe it , a home away from home for the literate heart .\"]", "output": "[['place', 'restaurant general', 'positive', 'try it and believe it']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Fish is so very fresh .']", "output": "[['Fish', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My boyfriend had Prime Rib it was good .']", "output": "[['Prime Rib', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Drinks way over priced .']", "output": "[['Drinks', 'drinks prices', 'negative', 'over priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Unique apppetizers .']", "output": "[['apppetizers', 'food quality', 'positive', 'Unique']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's a great place to order from or sit-in .\"]", "output": "[['place', 'restaurant miscellaneous', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is great and reasonably priced .']", "output": "[['food', 'food quality', 'positive', 'great'], ['food', 'food prices', 'positive', 'reasonably priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When we sat , we got great and fast service .']", "output": "[['service', 'service general', 'positive', 'great'], ['service', 'service general', 'positive', 'fast']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Be sure to try the seasonal , and always delicious , specials .']", "output": "[['specials', 'food quality', 'positive', 'try'], ['specials', 'food quality', 'positive', 'seasonal'], ['specials', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When we threatened to leave , we were offered a meager discount even though half the order was missing .']", "output": "[['NULL', 'service general', 'negative', 'missing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food , good size menu , great service and an unpretensious setting .']", "output": "[['food', 'food quality', 'positive', 'Great'], ['menu', 'food style_options', 'positive', 'good size'], ['service', 'service general', 'positive', 'great'], ['setting', 'ambience general', 'positive', 'unpretensious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sangria was pretty tasty and good on a hot muggy day .']", "output": "[['sangria', 'drinks quality', 'positive', 'tasty'], ['sangria', 'drinks quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Steamed fresh so brought hot hot hot to your table .']", "output": "[['NULL', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It just was n't Thai .\"]", "output": "[['NULL', 'food quality', 'negative', \"was n't Thai\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['All the staff is absolutely professional ! !']", "output": "[['staff', 'service general', 'positive', 'professional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .']", "output": "[['service', 'service general', 'positive', 'impeccable'], ['service', 'service general', 'positive', 'unobtrusive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A little pricey but it really hits the spot on a Sunday morning !']", "output": "[['NULL', 'restaurant prices', 'negative', 'pricey '], ['NULL', 'restaurant general', 'positive', 'hits the spot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The Four Seasons has history and it is a sort of landmark of New York City restaurants , but trust me , they will charge you through the nose just so that you can say `` I 've been to the four seasons restaurant `` .\"]", "output": "[['The Four Seasons', 'restaurant miscellaneous', 'neutral', 'a sort of landmark'], ['The Four Seasons', 'restaurant prices', 'negative', 'charge you through the nose']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This was a great surprise .']", "output": "[['NULL', 'restaurant general', 'positive', 'a great surprise']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like .\"]", "output": "[['food', 'food prices', 'negative', 'pricey'], ['prixe fixe tasting menu', 'food quality', 'positive', 'greatest'], ['prixe fixe tasting menu', 'food prices', 'positive', 'greatest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'm not picky - but it was actually gross .\"]", "output": "[['NULL', 'food quality', 'negative', 'gross']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food was very good , but not what I would consider out of this world .']", "output": "[['Food', 'food quality', 'neutral', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The server was really cool and served us our food and drinks with a smile .']", "output": "[['server', 'service general', 'positive', 'cool']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The manager came to the table and said we can do what we want , so we paid for what we did enjoy , the drinks and appetizers , and walked out .']", "output": "[['drinks', 'drinks quality', 'positive', 'enjoy'], ['appetizers', 'food quality', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is the most Japanese it can ever get .']", "output": "[['place', 'restaurant miscellaneous', 'positive', 'Japanese']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The portions are large and the servers always surprise us with a different starter .']", "output": "[['portions', 'food style_options', 'positive', 'large']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I absolutely Loved this place .']", "output": "[['place', 'restaurant general', 'positive', 'Loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo-chinese food .']", "output": "[['space', 'restaurant miscellaneous', 'negative', 'limited'], ['indo-chinese food', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Nice value .']", "output": "[['NULL', 'restaurant prices', 'positive', 'Nice value']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I love it .']", "output": "[['NULL', 'restaurant general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They have a delicious banana chocolate dessert , as well as a great green tea tempura .']", "output": "[['banana chocolate dessert', 'food quality', 'positive', 'delicious'], ['green tea tempura', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['i love margherita pizza \u2013 i looove east village pizza']", "output": "[['east village pizza', 'restaurant general', 'positive', 'love'], ['margherita pizza', 'food quality', 'positive', 'looove']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Here the hot dog is elevated to the level of a real entree with numerous variations available .']", "output": "[['hot dog', 'food quality', 'positive', 'elevated'], ['hot dog', 'food style_options', 'positive', 'elevated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['my favorite place lol']", "output": "[['place', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The counter service is bad .']", "output": "[['counter service', 'service general', 'negative', 'bad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Had an awful experience at Casa la Femme on a Saturday dinner .']", "output": "[['Casa la Femme', 'restaurant general', 'negative', 'awful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Just because it 's cheap does NOT mean the portions are small or the food is nasty , IT IS GREAT !\"]", "output": "[['food', 'food quality', 'positive', 'GREAT'], ['NULL', 'restaurant prices', 'positive', 'cheap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .']", "output": "[['menu', 'food style_options', 'negative', 'limited'], ['NULL', 'food quality', 'positive', 'ultra fresh'], ['NULL', 'food style_options', 'positive', 'a work of food art']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My main concern was the sanity of the food that was being sent out to myself and others , but I would be lying is I said that as someone who has worked in restaurants since the age of fifteen I was expecting at least a minimal effort on the part of the restaurant to amend the situation .']", "output": "[['food', 'food quality', 'negative', 'concern']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Everyone must come here at least once .']", "output": "[['NULL', 'restaurant general', 'positive', 'come here at least once']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The crust is thin , the ingredients are fresh and the staff is friendly .']", "output": "[['crust', 'food quality', 'positive', 'thin'], ['staff', 'service general', 'positive', 'friendly'], ['ingredients', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Do not get the Go Go Hamburgers , no matter what the reviews say .']", "output": "[['Go Go Hamburgers', 'food quality', 'negative', 'Do not get']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great place , great value .']", "output": "[['place', 'restaurant general', 'positive', 'Great'], ['place', 'restaurant prices', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Even after they overcharged me the last time I was there .']", "output": "[['NULL', 'service general', 'negative', 'overcharged']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Yamato is an excellent place to go if youre not into sashimi , or if you have friends who doesnt like sushi much .']", "output": "[['Yamato', 'restaurant miscellaneous', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Everytime I decide to try another place on the UES , I get angry that I did n't just go to Zucchero Pomodori .\"]", "output": "[['Zucchero Pomodori', 'restaurant general', 'positive', \"did n't just go to\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['LLOOVVE THIS PLACE .']", "output": "[['PLACE', 'restaurant general', 'positive', 'LLOOVVE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The menu is very limited - i think we counted 4 or 5 entrees .']", "output": "[['menu', 'food style_options', 'negative', 'limited']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is one of my favorite spot , very relaxing the food is great all the times , celebrated my engagement and my wedding here , it was very well organized .']", "output": "[['NULL', 'restaurant general', 'positive', 'favorite'], ['NULL', 'ambience general', 'positive', 'relaxing'], ['food', 'food quality', 'positive', 'great'], ['NULL', 'restaurant miscellaneous', 'positive', 'well organized']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The decor however seems to be the distraction so you wo n't notice that you just payed 300 bucks for some cold eggplant that took 2 FRICKIN HOURS TO COME ! ! ! !\"]", "output": "[['decor', 'ambience general', 'neutral', 'distraction'], ['eggplant', 'food quality', 'negative', 'cold'], ['eggplant', 'food prices', 'negative', '300 bucks'], ['NULL', 'service general', 'negative', '2 FRICKIN HOURS']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Do n't go with a larger group than 4 !\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', \"Do n't go\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\"]", "output": "[['place', 'restaurant general', 'positive', 'amazed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Dessert : pure disaster .']", "output": "[['Dessert', 'food quality', 'negative', 'disaster']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Moules were excellent , lobster ravioli was VERY salty !']", "output": "[['Moules', 'food quality', 'positive', 'excellent'], ['lobster ravioli', 'food quality', 'negative', 'salty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The atmosphere is relaxed and casual .']", "output": "[['atmosphere', 'ambience general', 'positive', 'relaxed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food is excellent .']", "output": "[['Food', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was average to above-average ; the French Onion soup filling yet not overly impressive , and the desserts not brilliant in any way .']", "output": "[['food', 'food quality', 'positive', 'average to above-average'], ['French Onion soup', 'food quality', 'negative', 'not overly impressive'], ['desserts', 'food quality', 'negative', 'not brilliant']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The manager finally said he would comp the two glasses of wine ( which cost less than the food ) , and made it seem like a big concession .']", "output": "[['manager', 'service general', 'negative', 'seem like a big concession']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Unhygienic']", "output": "[['NULL', 'food quality', 'negative', 'Unhygienic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I never write on these sites but this restaurant is def worth commending !']", "output": "[['restaurant', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food here was mediocre at best .']", "output": "[['food', 'food quality', 'negative', 'mediocre']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .']", "output": "[['decor', 'ambience general', 'positive', 'vibrant'], ['decor', 'ambience general', 'positive', 'eye-pleasing'], ['semi-private boths', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .']", "output": "[['NULL', 'service general', 'negative', 'rush']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The decor is very simple but comfortable .']", "output": "[['decor', 'ambience general', 'positive', 'simple'], ['decor', 'ambience general', 'positive', 'comfortable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"For starters they delivered us someone else 's order .\"]", "output": "[['NULL', 'service general', 'negative', \"delivered us someone else 's order\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had fish and my husband had the filet - both of which exceeded our expectations .']", "output": "[['fish', 'food quality', 'positive', 'exceeded our expectations'], ['filet', 'food quality', 'positive', 'exceeded our expectations']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was attentive , yet discreet .']", "output": "[['service', 'service general', 'positive', 'attentive'], ['service', 'service general', 'positive', 'discreet']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"They tell me they are going to cover the garden in glass for the winter , so i 'm looking forward to going there on a snowy night to enjoy it .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We arrived and were seated immediately , which made us both happy .']", "output": "[['NULL', 'service general', 'positive', 'happy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I come here enjoy very much with husband .']", "output": "[['NULL', 'restaurant general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I loved it and would go again .']", "output": "[['NULL', 'restaurant general', 'positive', 'loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The restaurant has a Family feel , not least with regard to the portions which are enormous ; the veal alone could have single-handedly solved third world famine .']", "output": "[['restaurant', 'ambience general', 'positive', 'Family feel'], ['portions', 'food style_options', 'positive', 'enormous'], ['veal', 'food style_options', 'positive', 'have single-handedly solved third world famine']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I do n't think I would go again .\"]", "output": "[['NULL', 'restaurant general', 'negative', \"do n't think I would go again \"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away .\"]", "output": "[['pizza', 'food quality', 'positive', 'ashamed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Always great service !']", "output": "[['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Our experience did not ever get any better .']", "output": "[['NULL', 'restaurant general', 'negative', 'did not ever get any better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The farro salad and the mashed yukon potatoes were also extremely tasty .']", "output": "[['farro salad', 'food quality', 'positive', 'tasty'], ['mashed yukon potatoes', 'food quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The restaurant is cute but not upscale .']", "output": "[['restaurant', 'restaurant general', 'neutral', 'cute'], ['restaurant', 'restaurant general', 'neutral', 'not upscale']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We did tip , I guess the model /waitress just wanted more and complained to the manager .']", "output": "[['waitress', 'service general', 'negative', 'complained']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['And even more so unpleasant because it was so costly for such an unpleasant experience .']", "output": "[['NULL', 'restaurant general', 'negative', 'unpleasant'], ['NULL', 'restaurant prices', 'negative', 'costly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .']", "output": "[['braised lamb shank in red wine', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was so stunned , and I left the dinner hungry and majorly disappointing .']", "output": "[['NULL', 'restaurant general', 'negative', 'disappointing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['People are always friendly .']", "output": "[['People', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I recently tried Suan and I thought that it was great .']", "output": "[['Suan', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Too bad I had paid an extra $ 2 for the stone bowl .']", "output": "[['stone bowl', 'food prices', 'negative', 'bad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I like the ambience , it 's very dark and original .\"]", "output": "[['ambience', 'ambience general', 'positive', 'like'], ['ambience', 'ambience general', 'positive', 'dark'], ['ambience', 'ambience general', 'positive', 'original']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Pizza was a little soggy .']", "output": "[['Pizza', 'food quality', 'negative', 'soggy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['great food , great wine list , great service in a great neighborhood ...']", "output": "[['food', 'food quality', 'positive', 'great'], ['wine list', 'drinks style_options', 'positive', 'great'], ['service', 'service general', 'positive', 'great'], ['neighborhood', 'location general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If you want something really different than try Jekyll and Hyde .']", "output": "[['Jekyll and Hyde', 'restaurant general', 'positive', 'different']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is good .']", "output": "[['food', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was almost amused by the fact that she was turning away customers at 9pm on a Friday night because she `` had a BBQ to go to `` that night - WTF ? ?']", "output": "[['NULL', 'service general', 'negative', 'WTF']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My wife had barely touched that mess of a dish .']", "output": "[['dish', 'food quality', 'negative', 'mess']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .']", "output": "[['jelly fish', 'food quality', 'positive', 'recommend'], ['drunken chicken', 'food quality', 'positive', 'recommend'], ['soupy dumplings', 'food quality', 'positive', 'recommend'], ['stir fry blue crab', 'food quality', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\"]", "output": "[['people', 'service general', 'positive', 'beautiful'], ['food', 'food quality', 'negative', \"does n't quite match up\"], ['Thalia', 'ambience general', 'positive', 'beautiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"We wo n't go to this place again for a good meal .\"]", "output": "[['meal', 'food quality', 'negative', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['i happen to have a policy that goes along with a little bit of self-respect , which includes not letting a waiter intimidate me , i.e . make me feel bad asking for trivialities like water , or the check .']", "output": "[['waiter', 'service general', 'negative', 'bad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My friends settled for rice dishes , but we came back the following day to try the dim sum , which was good ... not outstanding , but good .']", "output": "[['dim sum', 'food quality', 'neutral', 'good'], ['dim sum', 'food quality', 'neutral', 'not outstanding']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The signs , the specials menus , food , and even all the waitstaff are ALL TOTALLY Japanese .']", "output": "[['signs', 'restaurant miscellaneous', 'positive', 'Japanese'], ['specials menus', 'food style_options', 'positive', 'Japanese'], ['food', 'food quality', 'positive', 'Japanese'], ['waitstaff', 'service general', 'positive', 'Japanese']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When we inquired about ports - the waitress listed off several but did not know taste variations or cost .']", "output": "[['waitress', 'service general', 'negative', 'did not know']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This establishment is the real deal .']", "output": "[['establishment', 'restaurant general', 'positive', 'real deal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Go there to relax and feel like your somewhere else .']", "output": "[['NULL', 'ambience general', 'positive', 'relax']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We also asked for Hooka six times and the waiter kept telling us one minute and never returning with the Hooka .']", "output": "[['waiter', 'service general', 'negative', 'asked for Hooka six times']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is worth an one-hour drive .']", "output": "[['place', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the waitstaffs are nice though .']", "output": "[['waitstaffs', 'service general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was exceptional .']", "output": "[['food', 'food quality', 'positive', 'exceptional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have never left a restaurant feeling as if i was abused , and wasted my hard earned money .']", "output": "[['restaurant', 'restaurant general', 'negative', 'abused'], ['restaurant', 'restaurant prices', 'negative', 'abused']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Kind , attentive wait staff .']", "output": "[['wait staff', 'service general', 'positive', 'Kind'], ['wait staff', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Decor is charming .']", "output": "[['Decor', 'ambience general', 'positive', 'charming']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waitress , seems to be more concerned of looking good than actually waitressing .']", "output": "[['waitress', 'service general', 'negative', 'more concerned of looking good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I have been to Roth 's twice and both times were very disappointing .\"]", "output": "[[\"Roth 's\", 'restaurant general', 'negative', 'disappointing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"When you add it all together , it just does n't seem worth it to me ... especially considering the prices .\"]", "output": "[['NULL', 'restaurant general', 'negative', \"does n't seem worth it\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We went here for lunch a couple of weeks ago on a Saturday , and I was thoroughly impressed with the food .']", "output": "[['food', 'food quality', 'positive', 'impressed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sake \u2019 s complimented the courses very well and is successfully easing me into the sake world .']", "output": "[['sake \u2019 s', 'drinks quality', 'positive', 'very well'], ['sake \u2019 s', 'drinks quality', 'positive', 'successfully']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Go hungry and enjoy .']", "output": "[['NULL', 'restaurant general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\"]", "output": "[['martini', 'drinks quality', 'positive', 'delish'], ['martini', 'drinks style_options', 'positive', 'delish'], ['martini', 'drinks prices', 'positive', 'delish'], ['Vanilla Shanty', 'drinks quality', 'positive', 'recommend'], ['setting', 'ambience general', 'positive', 'homey'], ['music', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['More Williamsburg Garbage']", "output": "[['NULL', 'restaurant general', 'negative', 'Garbage']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is an amazing place to try some roti rolls .']", "output": "[['roti rolls', 'food quality', 'positive', 'try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Judging from previous posts this used to be a good place , but not any longer .']", "output": "[['place', 'restaurant general', 'negative', 'used to be a good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I wish they would change back to what it was before .']", "output": "[['NULL', 'restaurant general', 'negative', 'change back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It is quite a spectacular scene i 'll give them that .\"]", "output": "[['scene', 'ambience general', 'positive', 'spectacular']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A Thai restaurant out of rice during dinner ?']", "output": "[['Thai restaurant', 'restaurant miscellaneous', 'negative', 'out of rice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The bar is very well stocked with interesting beers and well priced wines .']", "output": "[['bar', 'drinks style_options', 'positive', 'well stocked'], ['beers', 'drinks style_options', 'positive', 'interesting'], ['wines', 'drinks prices', 'positive', 'well priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff there is very attentive and down to earth .']", "output": "[['staff', 'service general', 'positive', 'attentive'], ['staff', 'service general', 'positive', 'down to earth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The kitchen however , is almost always slow .']", "output": "[['kitchen', 'service general', 'negative', 'slow']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Loved it !']", "output": "[['NULL', 'restaurant general', 'positive', 'Loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It may be a bit packed on weekends , but the vibe is good and it is the best French food you will find in the area .']", "output": "[['NULL', 'ambience general', 'neutral', 'packed'], ['vibe', 'ambience general', 'positive', 'good'], ['French food', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Our teenage kids love it , too .']", "output": "[['NULL', 'restaurant general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['i love their chicken pasta cant remember the name but is sooo good']", "output": "[['chicken pasta', 'food quality', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not only is it an adventure getting to this somewhat hidden spot , once you enter the unmarked wooden doors , the zen and intimate d\u00e9cor will make you feel like you \u2019 re no longer in the city .']", "output": "[['spot', 'location general', 'neutral', 'hidden'], ['unmarked wooden doors', 'ambience general', 'positive', 'feel like you \u2019 re no longer in the city'], ['d\u00e9cor', 'ambience general', 'positive', 'intimate']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['great toppings definitely a place you need to check out for late night munchies or a mid day boost !']", "output": "[['toppings', 'food quality', 'positive', 'great'], ['place', 'restaurant miscellaneous', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A real dissapointment .']", "output": "[['NULL', 'food quality', 'negative', 'dissapointment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is incredibly tiny .']", "output": "[['place', 'ambience general', 'negative', 'tiny']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food and service were fine , however the maitre-D was incredibly unwelcoming and arrogant .']", "output": "[['food', 'food quality', 'positive', 'fine'], ['service', 'service general', 'positive', 'fine'], ['maitre-D', 'service general', 'negative', 'unwelcoming'], ['maitre-D', 'service general', 'negative', 'arrogant']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The duck confit is always amazing and the foie gras terrine with figs was out of this world .']", "output": "[['foie gras terrine with figs', 'food quality', 'positive', 'out of this world'], ['duck confit', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Most importantly , food is excellent .']", "output": "[['food', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I ordered the smoked salmon and roe appetizer and it was off flavor .']", "output": "[['smoked salmon and roe appetizer', 'food quality', 'negative', 'off flavor']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Maggot in the food !']", "output": "[['food', 'food quality', 'negative', 'Maggot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Salads were fantastic .']", "output": "[['Salads', 'food quality', 'positive', 'fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We had the lobster sandwich and it was FANTASTIC .']", "output": "[['lobster sandwich', 'food quality', 'positive', 'FANTASTIC']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I thought this place was totally overrated .']", "output": "[['place', 'restaurant general', 'negative', 'overrated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've lived in NY for 5 years and this place has it all .\"]", "output": "[['place', 'restaurant general', 'positive', 'has it all']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Nobody at this restaurant will give firm answers about anything and in the end , not one person takes responsibility for anything .']", "output": "[['NULL', 'service general', 'negative', 'not one person takes responsibility for anything']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ask for Usha , the nicest bartender in manhattan .']", "output": "[['Usha', 'service general', 'positive', 'nicest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"They forgot a sandwich , did n't include plastic forks , and did n't include pita with the hummus platter .\"]", "output": "[['NULL', 'service general', 'negative', 'forgot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The service was the only thing good about this restaurant .']", "output": "[['service', 'service general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We waited at the bar and had martinis that were just right .']", "output": "[['martinis', 'drinks quality', 'positive', 'right']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Thius is a must for anyone who loves Shabu-Shabu .']", "output": "[['Shabu-Shabu', 'food quality', 'positive', 'loves']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It was totally overpriced - fish and chips was about $ 15 ...']", "output": "[['NULL', 'food prices', 'negative', 'overpriced'], ['fish and chips', 'food prices', 'negative', 'about $ 15']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They have a huge selection of different cream cheeses and all of their salads are great .']", "output": "[['salads', 'food quality', 'positive', 'great'], ['cream cheeses', 'food style_options', 'positive', 'huge'], ['cream cheeses', 'food style_options', 'positive', 'different']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Very pleased']", "output": "[['NULL', 'restaurant general', 'positive', 'pleased']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I would not expect this for a $ 55 dinner .']", "output": "[['dinner', 'food quality', 'negative', 'would not expect'], ['dinner', 'food prices', 'negative', '$ 55']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I got hair in my food 2 times of then !']", "output": "[['food', 'food quality', 'negative', 'got hair in my food']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Noodle pudding is exactly the type of service and food I enjoy .']", "output": "[['service', 'service general', 'positive', 'enjoy'], ['food', 'food quality', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Volare virgins or weekly regulars , everyone gets treated the same and you ca n't ask for more than that when the service is this friendly .\"]", "output": "[['service', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Ive asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away .']", "output": "[['cart attendant', 'service general', 'negative', 'walked away']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is always very crowded and popular .']", "output": "[['place', 'restaurant miscellaneous', 'positive', 'crowded'], ['place', 'restaurant miscellaneous', 'positive', 'popular']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The design and atmosphere is just as good .']", "output": "[['design', 'ambience general', 'positive', 'good'], ['atmosphere', 'ambience general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Interesting selection , good wines , service fine , fun decor .']", "output": "[['wines', 'drinks quality', 'positive', 'good'], ['service', 'service general', 'positive', 'fine'], ['decor', 'ambience general', 'positive', 'fun'], ['selection', 'food style_options', 'positive', 'Interesting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['first it took us a long time to find the place .']", "output": "[['place', 'restaurant miscellaneous', 'negative', 'took us a long time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The manager was rude and handled the situation extremely poorly .']", "output": "[['manager', 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But the best pork souvlaki I ever had is the main thing .']", "output": "[['pork souvlaki', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I wish I could like this place more , and I wish someone would retrain the staff .']", "output": "[['staff', 'service general', 'negative', 'retrain']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Also , they do not take credit card so come with cash !']", "output": "[['NULL', 'restaurant miscellaneous', 'neutral', 'come with cash']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Every time I have a special occasion with my boyfriend I have a hard time going anywhere else .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'have a hard time going anywhere else']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Dumbfoundingly Poor']", "output": "[['NULL', 'restaurant general', 'negative', 'Dumbfoundingly Poor']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food , great prices , great service .']", "output": "[['food', 'food quality', 'positive', 'Great'], ['NULL', 'restaurant prices', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My son and his girlfriend both wanted cheeseburgers and they were huge !']", "output": "[['cheeseburgers', 'food style_options', 'neutral', 'huge']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good for dates or with friends .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'Good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I CAN EAT HERE EVERY DAY OF THE WEEK REALLY LOL LOVE THIS PLACE ... )']", "output": "[['PLACE', 'restaurant general', 'positive', 'LOVE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This guy refused to seat her and she left , followed shortly by the four of us , but not before I told him that in my 40 years of world travel , including Paris , that I had never seen such a display of bad behavior by a frontman in a restaurant .']", "output": "[['frontman', 'service general', 'negative', 'bad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Thank you everyone at Water 's Edge .\"]", "output": "[[\"Water 's Edge\", 'restaurant general', 'positive', 'Thank you']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Overall , decent food at a good price , with friendly people .']", "output": "[['food', 'food quality', 'positive', 'decent'], ['food', 'food prices', 'positive', 'good'], ['people', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I showed it to the manager , and he smilingly apologized and brought us two free desserts ( but did not ask us what we wanted and so brought the last two desserts we would have asked for ) .']", "output": "[['manager', 'service general', 'positive', 'smilingly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service is average .']", "output": "[['Service', 'service general', 'neutral', 'average']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I LOVE their Thai']", "output": "[['Thai', 'food quality', 'positive', 'LOVE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waitress moved our table practically into the bathroom and when we asked to cancel our dinner orders because we did not want to eat sitting on the toilet , we were told no ...']", "output": "[['waitress', 'service general', 'negative', 'sitting on the toilet']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Probably would not go again ...']", "output": "[['NULL', 'restaurant general', 'negative', 'not go again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Tiny dessert was $ 8.00 ... just plain overpriced for what it is .']", "output": "[['dessert', 'food quality', 'negative', 'plain'], ['dessert', 'food style_options', 'negative', 'Tiny'], ['dessert', 'food prices', 'negative', 'overpriced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Do n't dine at Tamarind for the vegetarian dishes , they are simply not up to par with the non-veg selections .\"]", "output": "[['vegetarian dishes', 'food quality', 'negative', 'not up to par']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['And it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3-6 each ) .']", "output": "[['NULL', 'food prices', 'positive', 'reasonable'], ['congee', 'food prices', 'positive', 'no more than $ 3-6 each'], ['noodles', 'food prices', 'positive', 'no more than $ 3-6 each'], ['rice dishes', 'food prices', 'positive', 'no more than $ 3-6 each']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The cold appetizer dishes taste like the way I remember them to taste when I was growing up in Taiwan .']", "output": "[['cold appetizer dishes', 'food quality', 'positive', 'like the way I remember them']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The appetizers are also delicious !']", "output": "[['appetizers', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine list is extensive and can easily hike up an otherwise reasonably priced meal .']", "output": "[['wine list', 'drinks style_options', 'positive', 'extensive'], ['meal', 'food prices', 'positive', 'reasonably priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Please take my advice , go and try this place .']", "output": "[['place', 'restaurant general', 'positive', 'go and try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Decor needs to be upgraded but the food is amazing !']", "output": "[['Decor', 'ambience general', 'negative', 'upgraded'], ['food', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Single Worst Restaurant in Manhattan']", "output": "[['Restaurant', 'restaurant general', 'negative', 'Worst']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Place is open till late , no dress code .']", "output": "[['Place', 'restaurant miscellaneous', 'positive', 'open till late']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\"]", "output": "[['Edamame pureed', 'food quality', 'positive', 'tasty'], ['Edamame pureed', 'food style_options', 'positive', 'innovative']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Open late ( well as late as I ever got there and I 'm a night person )\"]", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'well']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The only thing I moderately enjoyed was their Grilled Chicken special with Edamame Puree .']", "output": "[['Grilled Chicken special with Edamame Puree', 'food quality', 'positive', 'enjoyed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I do n't appreciate places or people that try to drive up the bill without the patron 's knowledge so that was a huge turnoff ( more than the price ) .\"]", "output": "[['NULL', 'service general', 'negative', 'drive up the bill'], ['NULL', 'restaurant prices', 'negative', 'huge turnoff']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Love their drink menu .']", "output": "[['drink menu', 'drinks style_options', 'positive', 'Love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Bukhara is on my top 5 Indian places in NYC']", "output": "[['Bukhara', 'restaurant general', 'positive', 'top']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .']", "output": "[['location', 'location general', 'positive', 'good'], ['Suan', 'restaurant prices', 'positive', 'reasonable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This was , from start to finish , a mind-bogglingly uncomfortable experience .']", "output": "[['NULL', 'restaurant general', 'negative', 'mind-bogglingly uncomfortable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Terrible , terrible management - deserves to be shut-down .']", "output": "[['management', 'service general', 'negative', 'Terrible'], ['management', 'service general', 'negative', 'terrible'], ['NULL', 'restaurant general', 'negative', 'deserves to be shut-down']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I took one look at the chicken and I was appalled .']", "output": "[['chicken', 'food style_options', 'negative', 'appalled']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .']", "output": "[['Appetizers', 'food quality', 'neutral', 'ok'], ['waiter', 'service general', 'negative', 'poor'], ['potato stuff kanish', 'food quality', 'positive', 'try'], ['potato stuff kanish', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The bagel was huge .']", "output": "[['bagel', 'food style_options', 'positive', 'huge']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My husband and I have been sold on this from the first visit .']", "output": "[['NULL', 'restaurant general', 'positive', 'sold on']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Excellent food , although the interior could use some help .']", "output": "[['food', 'food quality', 'positive', 'Excellent'], ['interior', 'ambience general', 'negative', 'help']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I look forward to eating here again']", "output": "[['NULL', 'restaurant general', 'positive', 'look forward']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I went to Areo on a Sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .']", "output": "[['Areo', 'restaurant general', 'positive', 'enjoyable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service was very prompt but slightly rushed .']", "output": "[['Service', 'service general', 'positive', 'prompt'], ['Service', 'service general', 'positive', 'rushed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['bottles of wine are cheap and good .']", "output": "[['bottles of wine', 'drinks prices', 'positive', 'cheap'], ['bottles of wine', 'drinks quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The menu has so many fish items and oysters .']", "output": "[['menu', 'food style_options', 'positive', 'so many']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"They 're rude at times , and not very friendly .\"]", "output": "[['NULL', 'service general', 'negative', 'rude']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was absolutely amazing ! !']", "output": "[['food', 'food quality', 'positive', 'amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Terrible would be a compliment !']", "output": "[['NULL', 'restaurant general', 'negative', 'Terrible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The menu looked great , and the waiter was very nice , but when the food came , it was average .']", "output": "[['menu', 'food style_options', 'positive', 'great'], ['waiter', 'service general', 'positive', 'nice'], ['food', 'food quality', 'neutral', 'average']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were greeted promptly by the waiter who was very nice and cordial .']", "output": "[['waiter', 'service general', 'positive', 'nice'], ['waiter', 'service general', 'positive', 'cordial']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Appetizers took nearly an hour .']", "output": "[['NULL', 'service general', 'negative', 'nearly an hour']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great find in the West Village !']", "output": "[['NULL', 'restaurant general', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I wo n't go back unless someone else is footing the bill .\"]", "output": "[['NULL', 'restaurant prices', 'negative', \"wo n't go back\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wine list selection is good and wine-by-the-glass was generously filled to the top .']", "output": "[['Wine list selection', 'drinks style_options', 'positive', 'good'], ['wine-by-the-glass', 'drinks style_options', 'positive', 'generously filled']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The people that work there are always so friendly you forget you are in New York sometimes .']", "output": "[['people', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Decent wine at reasonable prices .']", "output": "[['wine', 'drinks quality', 'positive', 'Decent'], ['wine', 'drinks prices', 'positive', 'reasonable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I love it .']", "output": "[['NULL', 'restaurant general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I got the $ 10 10-piece dim sum combo , every bite of which was great .']", "output": "[['$ 10 10-piece dim sum combo', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"DO not try unless you 're just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'Do not try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great food !']", "output": "[['food', 'food quality', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Their pad penang is delicious and everything else is fantastic .']", "output": "[['pad penang', 'food quality', 'positive', 'delicious'], ['NULL', 'food quality', 'positive', 'fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I can not imagine a friendlier staff working in a restaurant .']", "output": "[['staff', 'service general', 'positive', 'friendlier']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Luckily we saved room for the BBQ Salmon , Sea Bass and Crispy Duck .']", "output": "[['BBQ Salmon', 'food quality', 'positive', 'Luckily'], ['Sea Bass', 'food quality', 'positive', 'Luckily'], ['Crispy Duck', 'food quality', 'positive', 'Luckily']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Dokebi gives Williamsburg the right one-two punch of classic Korean food and fusion twists like pork belly tacos .']", "output": "[['Korean food', 'food quality', 'positive', 'classic'], ['fusion twists', 'food quality', 'positive', 'classic'], ['pork belly tacos', 'food quality', 'positive', 'classic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"If you venture off the island of Manhattan and ca n't seem to find a great Italian restaurant , drive to Corona .\"]", "output": "[['Corona', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We all felt it was worth it .']", "output": "[['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Lucky Strike is a great casual place to just grab a bite to eat .']", "output": "[['Lucky Strike', 'restaurant general', 'positive', 'great casual']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have been to Casimir over 5 times and I have always had a great time there .']", "output": "[['Casimir', 'restaurant general', 'positive', 'great time']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['great for a romantic evening , or a fun evening with friends ...']", "output": "[['NULL', 'ambience general', 'positive', 'romantic'], ['NULL', 'restaurant miscellaneous', 'positive', 'fun']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But the best part about LS is the late night atmosphere , delightfully free of the BTs .']", "output": "[['late night atmosphere', 'ambience general', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The dining room is quietly elegant with no music to shout over -- how refreshing !']", "output": "[['dining room', 'ambience general', 'positive', 'elegant'], ['dining room', 'ambience general', 'positive', 'refreshing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is such a lovely , peaceful place to eat outside .']", "output": "[['place', 'ambience general', 'positive', 'lovely'], ['place', 'ambience general', 'positive', 'peaceful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Do n't be fooled by crowds of people .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'fooled']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['As to my comment about the food , no apology or acknowledgment was made .']", "output": "[['NULL', 'service general', 'negative', 'no apology or acknowledgment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Service is top notch .']", "output": "[['Service', 'service general', 'positive', 'top notch']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['oh speaking of bathroom , the mens bathroom was disgusting .']", "output": "[['mens bathroom', 'ambience general', 'negative', 'disgusting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\"]", "output": "[['decor', 'ambience general', 'positive', 'cool'], ['pita', 'food quality', 'positive', 'good'], ['hummus', 'food quality', 'positive', 'good'], ['grilled octopus', 'food quality', 'positive', 'tasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .']", "output": "[['NULL', 'restaurant general', 'positive', 'DO NOT pass it up']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .']", "output": "[['raddichio', 'food quality', 'negative', 'bitter'], ['NULL', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If I could give 0 stars I would do so for this place .']", "output": "[['place', 'restaurant general', 'negative', '0 stars']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Amazing !']", "output": "[['NULL', 'food quality', 'positive', 'Amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The hostess is rude to the point of being offensive .']", "output": "[['hostess', 'service general', 'negative', 'rude'], ['hostess', 'service general', 'negative', 'offensive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The frizzy retro girl ( with winged/ Dame Edna glasses ) will yell at you if you try to order a drink .']", "output": "[['girl', 'service general', 'negative', 'frizzy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['peppers , onions , relish , chilli , cheeses , you NAME it .']", "output": "[['NULL', 'food style_options', 'positive', 'you NAME it']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great atmoshere and worth every bit .']", "output": "[['atmoshere', 'ambience general', 'positive', 'Great'], ['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Bison was quite excellent however .']", "output": "[['Bison', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .']", "output": "[['food', 'food quality', 'positive', 'diamond'], ['balance of herbs and tomatoes', 'food quality', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It hits the spot every time']", "output": "[['NULL', 'restaurant general', 'positive', 'hits the spot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .']", "output": "[['Food', 'food quality', 'positive', 'good'], ['view of the new york city skiline', 'location general', 'positive', 'terrific']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lava cake dessert was incredible and I recommend it .']", "output": "[['lava cake dessert', 'food quality', 'positive', 'incredible'], ['lava cake dessert', 'food quality', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['service is friendly , and never had a problem walking in and getting a table .']", "output": "[['service', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I will be out with friends and all of a sudden I am hungry and I only crave one thing ... their Pizza .']", "output": "[['Pizza', 'food quality', 'positive', 'crave']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was here a few weeks back and we had the worst customer service experience at a restaurant ever .']", "output": "[['customer service', 'service general', 'negative', 'worst']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The spicy tuna roll was unusually good and the rock shrimp tempura was awesome , great appetizer to share !']", "output": "[['spicy tuna roll', 'food quality', 'positive', 'good'], ['rock shrimp tempura', 'food quality', 'positive', 'awesome']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The location and ambience is Ok but the food is what makes up for it .']", "output": "[['location', 'location general', 'neutral', 'Ok'], ['ambience', 'ambience general', 'neutral', 'Ok'], ['food', 'food quality', 'positive', 'makes up for it']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I noticed alot of indian people eatting there which is a great sign for an indian place !']", "output": "[['indian place', 'restaurant miscellaneous', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Always a nice crowd , but never loud .']", "output": "[['crowd', 'restaurant miscellaneous', 'positive', 'nice'], ['NULL', 'ambience general', 'positive', 'never loud']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['we love th pink pony .']", "output": "[['pink pony', 'restaurant general', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Nevertheless , I finished my plate , and that 's when I found a maggot in mushroom sauce at the bottom .\"]", "output": "[['mushroom sauce', 'food quality', 'negative', 'maggot']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I started out with a Bombay beer which was big enough for two .']", "output": "[['Bombay beer', 'drinks style_options', 'positive', 'big']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The view is spectacular , and the food is great .']", "output": "[['view', 'location general', 'positive', 'spectacular'], ['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['What really makes it shine is the food , which is aggressively seasoned with Cyrpriot spices , and all made in-house ( even the gyro meat and sausages ) , and made of much higher quality ingredients that might otherwise be expected .']", "output": "[['food', 'food quality', 'positive', 'shine'], ['gyro meat', 'food quality', 'positive', 'in-house'], ['sausages', 'food quality', 'positive', 'in-house'], ['ingredients', 'food quality', 'positive', 'higher quality']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['What a great place .']", "output": "[['place', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Which of course is not real Kobe but Wagyu beef .']", "output": "[['NULL', 'food quality', 'negative', 'not real']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Also , specify if you like your food spicy- its rather bland if you do n't .\"]", "output": "[['food', 'food quality', 'negative', 'bland']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They were served warm and had a soft fluffy interior .']", "output": "[['NULL', 'food quality', 'positive', 'warm'], ['NULL', 'food quality', 'positive', 'a soft fluffy interior']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !']", "output": "[['svc', 'service general', 'negative', 'rude'], ['restaurant', 'restaurant general', 'positive', 'must']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had a great experience .']", "output": "[['NULL', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['MMMMMMMMMmmmmmm so delicious']", "output": "[['NULL', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It is thick and slightly soggy .']", "output": "[['NULL', 'food quality', 'negative', 'thick'], ['NULL', 'food quality', 'negative', 'soggy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The first time the sushi was outstanding , the second time it was a little bland .']", "output": "[['sushi', 'food quality', 'positive', 'outstanding'], ['sushi', 'food quality', 'negative', 'bland']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Authentic Shanghai style and I can not recommend a better Shanghai place in New York .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'can not recommend a better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is flavorful , plentiful and reasonably priced .']", "output": "[['food', 'food quality', 'positive', 'flavorful'], ['food', 'food style_options', 'positive', 'plentiful'], ['food', 'food prices', 'positive', 'reasonably priced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Have frequented 'ino for several years and the food remains excellent .\"]", "output": "[['food', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Gorgeous place ideal for a romantic dinner']", "output": "[['place', 'ambience general', 'positive', 'Gorgeous'], ['place', 'restaurant miscellaneous', 'positive', 'ideal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['quacamole at pacifico is yummy , as are the wings with chimmichuri .']", "output": "[['quacamole', 'food quality', 'positive', 'yummy'], ['wings with chimmichuri', 'food quality', 'positive', 'yummy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Cute and decorative .']", "output": "[['NULL', 'ambience general', 'positive', 'Cute'], ['NULL', 'ambience general', 'positive', 'decorative']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I found the food to be outstanding , particulary the salmon dish I had .']", "output": "[['food', 'food quality', 'positive', 'outstanding'], ['salmon dish', 'food quality', 'positive', 'outstanding']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My wife had the fried shrimp which are huge and loved it .']", "output": "[['fried shrimp', 'food style_options', 'positive', 'huge'], ['fried shrimp', 'food quality', 'positive', 'loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Try sushimi cucumber roll .']", "output": "[['sushimi cucumber roll', 'food quality', 'positive', 'Try']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Amazing food .']", "output": "[['food', 'food quality', 'positive', 'Amazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I do not recommend .']", "output": "[['NULL', 'restaurant general', 'negative', 'do not recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Truly the mark of an attentive waiter .']", "output": "[['waiter', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Wouldn 't recomend it for dinner !\"]", "output": "[['NULL', 'restaurant general', 'negative', \"Wouldn 't recomend\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The pizza was delivered cold and the cheese was n't even fully melted !\"]", "output": "[['pizza', 'food quality', 'negative', 'cold'], ['cheese', 'food quality', 'negative', \"was n't even fully melted\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['the last time i walked by it looked pretty empty . hmmm .']", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'empty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'd be horrified if my staff were turning away customers so early and so rudely !\"]", "output": "[['staff', 'service general', 'negative', 'horrified']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Which lets face it ... .at times it 's a good thing .\"]", "output": "[['NULL', 'service general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The waiter delivered our food while holding what appeared to be a plastic bag of garbage in one hand .']", "output": "[['waiter', 'service general', 'negative', 'garbage']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You can not go wrong with this place .']", "output": "[['place', 'restaurant general', 'positive', 'wrong']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I am so coming back here again , as much as I can .']", "output": "[['NULL', 'restaurant general', 'positive', 'coming back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We paid a fixed pricce but got nothing ! !']", "output": "[['NULL', 'service general', 'negative', 'got nothing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .']", "output": "[['chicken casserole', 'food quality', 'negative', 'all dark meat and on the bone '], ['chicken casserole', 'food style_options', 'negative', 'small']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When I got there the place was packed but they made sure to seat me quickly .']", "output": "[['NULL', 'service general', 'positive', 'seat me quickly ']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The sushi was awful !']", "output": "[['sushi', 'food quality', 'negative', 'awful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Slightly on the pricey side but worth it !']", "output": "[['NULL', 'restaurant prices', 'negative', 'pricey'], ['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff is no nonsense .']", "output": "[['staff', 'service general', 'positive', 'no nonsense']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['its alright']", "output": "[['NULL', 'restaurant general', 'neutral', 'alright']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['All of the pizzas are terrific and the price is even better !']", "output": "[['pizzas', 'food quality', 'positive', 'terrific'], ['NULL', 'restaurant prices', 'positive', 'even better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Cute place , nice wait staff but would never go there again .']", "output": "[['wait staff', 'service general', 'positive', 'nice'], ['place', 'ambience general', 'positive', 'Cute'], ['place', 'restaurant general', 'negative', 'never go']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The entree was bland and small , dessert was not inspired .']", "output": "[['entree', 'food quality', 'negative', 'bland'], ['entree', 'food style_options', 'negative', 'small'], ['dessert', 'food quality', 'negative', 'not inspired']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Our family never expected such incredible entertainment in a restaurant .']", "output": "[['entertainment', 'ambience general', 'positive', 'incredible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['noodles with shrimp and chicken and coconut juice is the MUST !']", "output": "[['noodles with shrimp and chicken and coconut juice', 'food quality', 'positive', 'MUST']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The scallion pancakes and fried dumplings were nothing out of the ordinary .']", "output": "[['scallion pancakes', 'food quality', 'neutral', 'ordinary'], ['fried dumplings', 'food quality', 'neutral', 'ordinary']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I really liked this place .']", "output": "[['place', 'restaurant general', 'positive', 'liked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Super YUMMY Pizza !']", "output": "[['Pizza', 'food quality', 'positive', 'YUMMY']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have NEVER been disappointed in the Red Eye .']", "output": "[['Red Eye', 'restaurant general', 'positive', 'NEVER been disappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Will absolutely visit again .']", "output": "[['NULL', 'restaurant general', 'positive', 'visit again']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I ordered the vitello alla marsala and I was pretty impressed .']", "output": "[['vitello alla marsala', 'food quality', 'positive', 'impressed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Quick and friendly service .']", "output": "[['service', 'service general', 'positive', 'Quick'], ['service', 'service general', 'positive', 'friendly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Once we finally got a table , despite indicating we wanted an alla carte menu we were pushed into a table that was only price fixed !']", "output": "[['NULL', 'service general', 'negative', 'pushed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Warning : You may find it difficult to dine at other Japanese restaurants after a visit to Mizu !']", "output": "[['Mizu', 'restaurant general', 'positive', 'difficult']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['What a great place !']", "output": "[['place', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Myagi is one of my favorite restaurants in the City ; the place the negative reviews describe sound like they were somewhere else .']", "output": "[['Myagi', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Cool atmosphere , the fire place in the back really ads to it but needs a bit more heat throughout on a cold night .']", "output": "[['atmosphere', 'ambience general', 'positive', 'Cool'], ['fire place', 'ambience general', 'positive', 'really ads to it']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .']", "output": "[['atmosphere', 'ambience general', 'positive', 'nothing special']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The tuna and wasabe potatoes are excellent .']", "output": "[['tuna', 'food quality', 'positive', 'excellent'], ['wasabe potatoes', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Have never had a problem with service save a missing rice once .']", "output": "[['service', 'service general', 'positive', 'problem']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were worried we would have trouble getting in , but somehow managed to have a short wait .']", "output": "[['wait', 'service general', 'positive', 'short']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Inside is a little cramped , but to be expected .']", "output": "[['NULL', 'ambience general', 'neutral', 'to be expected']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was pleasantly surprised to find this gem in Hoboken .']", "output": "[['NULL', 'restaurant general', 'positive', 'pleasantly surprised']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The appetizers we ordered were served quickly - an order of fried oysters and clams were delicious but a tiny portion ( maybe 3 of each ) .']", "output": "[['fried oysters and clams', 'food quality', 'positive', 'delicious'], ['fried oysters and clams', 'food style_options', 'negative', 'tiny portion'], ['NULL', 'service general', 'positive', 'served quickly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I ca n't wait for summer , when they serve outside on their gigantic patio .\"]", "output": "[['patio', 'ambience general', 'positive', 'gigantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Keep up the good work guys !']", "output": "[['NULL', 'restaurant general', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['stick with the chicken , beef , and lamb dishes .']", "output": "[['chicken', 'food quality', 'positive', 'stick'], ['beef', 'food quality', 'positive', 'stick'], ['lamb dishes', 'food quality', 'positive', 'stick']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A little overpriced but worth it once you take a bite .']", "output": "[['NULL', 'food prices', 'negative', 'overpriced'], ['NULL', 'food quality', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Over all it was a very nice romantic place .']", "output": "[['place', 'restaurant general', 'positive', 'nice romantic'], ['place', 'ambience general', 'positive', 'nice romantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Rather than preparing vegetarian dish , the chef presented me with a plate of steamed vegetables ( minus sauce , seasoning , or any form or aesthetic presentation ) .']", "output": "[['vegetarian dish', 'food style_options', 'negative', 'minus sauce , seasoning , or any form or aesthetic presentation']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Good , fast service .']", "output": "[['service', 'service general', 'positive', 'Good'], ['service', 'service general', 'positive', 'fast']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A big disappointment , all around .']", "output": "[['NULL', 'restaurant general', 'negative', 'disappointment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This is the BEST Shabu-Shabu Restaurant in the Try-State Area .']", "output": "[['Shabu-Shabu Restaurant', 'restaurant general', 'positive', 'BEST']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My boyfriend and I went there to celebrate my birthday the other night and all I can say is that it was magnificent .']", "output": "[['NULL', 'restaurant general', 'positive', 'magnificent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place was nice and calm .']", "output": "[['place', 'ambience general', 'positive', 'nice'], ['place', 'ambience general', 'positive', 'calm']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .']", "output": "[['lobster sandwich', 'food quality', 'positive', 'good'], ['spaghetti with Scallops and Shrimp', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .']", "output": "[['sushi', 'food quality', 'positive', 'great'], ['thai food', 'food quality', 'positive', 'excellent'], ['Planet Thailand', 'restaurant general', 'positive', 'hit']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['would NOT go back .']", "output": "[['NULL', 'restaurant general', 'negative', 'NOT go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were charged full price .']", "output": "[['NULL', 'service general', 'negative', 'full price']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Will comeback for sure , wish they have it here in LA . .']", "output": "[['NULL', 'restaurant general', 'positive', 'comeback']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['You can not go wrong at the Red Eye Grill .']", "output": "[['Red Eye Grill', 'restaurant general', 'positive', 'can not go wrong']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['All in all , the food was great ( except for the dessserts ) .']", "output": "[['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When family came in he gave them apps to test their palets , and then ordered for them .']", "output": "[['NULL', 'service general', 'positive', 'ordered for them']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['There is no excuse for such lousy service !']", "output": "[['service', 'service general', 'negative', 'lousy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Save your money and your time and go somewhere else .']", "output": "[['NULL', 'restaurant general', 'negative', 'go somewhere else']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We were very disappointed .']", "output": "[['NULL', 'food quality', 'negative', 'disappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Also , waiters try to push more food on you , like suggest things as if they are complimentary when they actually cost $ .']", "output": "[['waiters', 'service general', 'negative', 'push']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place itself is beautiful the bar scene seems to be happening .']", "output": "[['place', 'ambience general', 'positive', 'beautiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['For the people who want great food plus great service , Roxy is a place to AVOID !']", "output": "[['food', 'food quality', 'negative', 'great'], ['service', 'service general', 'negative', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['When we stumbled on Leon , we thought that we had found quite the gem BUT , we were certainly wrong .']", "output": "[['Leon', 'restaurant general', 'negative', 'certainly wrong']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The pizza is yummy and I like the atmoshpere .']", "output": "[['pizza', 'food quality', 'positive', 'yummy'], ['atmoshpere', 'ambience general', 'positive', 'like']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The Seafood Dynamite is also otherworldly .']", "output": "[['Seafood Dynamite', 'food quality', 'positive', 'otherworldly']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['this place had ALL the trimmings and i mean all .']", "output": "[['trimmings', 'food style_options', 'positive', 'ALL']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had the best ravioli ever .']", "output": "[['ravioli', 'food quality', 'positive', 'best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I can not imagine better Indian food in all of the city .']", "output": "[['Indian food', 'food quality', 'positive', 'better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Cozy romantic atomosphere with only around 15 tables at most .']", "output": "[['atomosphere', 'ambience general', 'positive', 'Cozy romantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Other guests enjoyed pizza , santa fe chopped salad and fish and chips .']", "output": "[['pizza', 'food quality', 'positive', 'enjoyed'], ['santa fe chopped salad', 'food quality', 'positive', 'enjoyed'], ['fish and chips', 'food quality', 'positive', 'enjoyed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Best Taiwanese food in NY !']", "output": "[['Taiwanese food', 'food quality', 'positive', 'Best']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was mediocre at best but it was the horrible service that made me vow never to go back .']", "output": "[['food', 'food quality', 'negative', 'mediocre'], ['service', 'service general', 'negative', 'horrible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If you are looking for a good quality , cheap eats - this is the place .']", "output": "[['eats', 'food quality', 'positive', 'good quality'], ['eats', 'food prices', 'positive', 'cheap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We ordered some beef and noodle soup dishes from the Thai section of the menu but nothing we got was Thai .']", "output": "[['beef and noodle soup dishes', 'food quality', 'negative', 'nothing we got was Thai']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The bibimbap was average , but the stone bowl was n't even close to sizzling .\"]", "output": "[['bibimbap', 'food quality', 'neutral', 'average'], ['stone bowl', 'food quality', 'negative', \"was n't even close to sizzling\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"This place , which is only a few months old , is perhaps Queens ' biggest secret !\"]", "output": "[['place', 'restaurant general', 'positive', \"Queens ' biggest secret\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Food was good not great not worth the wait or another visit']", "output": "[['Food', 'food quality', 'neutral', 'good not great not worth the wait or another visit']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not sure where the previous reviewer , lonk , dined , but Saul is in a great neighborhood and has great food !']", "output": "[['neighborhood', 'location general', 'positive', 'great'], ['food', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My friend got the mushroom pizza which tasted better .']", "output": "[['mushroom pizza', 'food quality', 'positive', 'better']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I paid just about $ 60 for a good meal , though : )']", "output": "[['meal', 'food quality', 'positive', 'good'], ['meal', 'food prices', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Nice view of river and NYC .']", "output": "[['view of river and NYC', 'location general', 'positive', 'Nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I found it on a cold night , the perfect spot to warm up .']", "output": "[['spot', 'restaurant miscellaneous', 'positive', 'perfect']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I was very disappointed with this restaurant .']", "output": "[['restaurant', 'restaurant general', 'negative', 'disappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['MMmmm ... it was delicious .']", "output": "[['NULL', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'd definitely go back again .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'go back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The omlette for brunch is great ...']", "output": "[['omlette for brunch', 'food quality', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The fried dumplings are GREAT !']", "output": "[['fried dumplings', 'food quality', 'positive', 'GREAT']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['If you are going for the food , it will not be worth it .']", "output": "[['food', 'food quality', 'negative', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Never again !']", "output": "[['NULL', 'restaurant general', 'negative', 'Never']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\"]", "output": "[['place', 'ambience general', 'negative', '*not* romantic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Got a date ? Go here !']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'Go here']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My fav was the sassy lassi ...']", "output": "[['sassy lassi', 'drinks quality', 'positive', 'fav']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['An excellent service']", "output": "[['service', 'service general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Highly impressed from the decor to the food to the hospitality to the great night I had !']", "output": "[['decor', 'ambience general', 'positive', 'impressed'], ['food', 'food quality', 'positive', 'impressed'], ['NULL', 'service general', 'positive', 'hospitality'], ['NULL', 'restaurant general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The place is beautiful !']", "output": "[['place', 'ambience general', 'positive', 'beautiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Worth visiting the 1st Ave spot because it is the original store .']", "output": "[['1st Ave spot', 'restaurant miscellaneous', 'positive', 'Worth visiting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Overall , excellent restaurant !']", "output": "[['restaurant', 'restaurant general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Fantastic !']", "output": "[['NULL', 'restaurant general', 'positive', 'Fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Mazing interior .']", "output": "[['interior', 'ambience general', 'negative', 'Mazing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A weakness is the chicken in the salads .']", "output": "[['chicken in the salads', 'food quality', 'negative', 'weakness']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Shame on this place for the horrible rude staff and non-existent customer service .']", "output": "[['staff', 'service general', 'negative', 'rude'], ['customer service', 'service general', 'negative', 'non-existent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['But too far east !']", "output": "[['NULL', 'location general', 'negative', 'too far']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We started off with a delightful sashimi amuse bouche .']", "output": "[['sashimi amuse bouche', 'food quality', 'positive', 'delightful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Maybe it was the great company ( I had friends visiting from Philly \u2013 yes , it was not a date this time ) or the super reasonable price point , but I just can \u2019 t say enough good things about this brasserie .']", "output": "[['brasserie', 'restaurant general', 'positive', 'good'], ['brasserie', 'restaurant prices', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's a nice place to relax and have conversation .\"]", "output": "[['place', 'ambience general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"But that was n't the icing on the cake : a tiramisu that resembled nothing I have ever had .\"]", "output": "[['tiramisu', 'food quality', 'negative', 'nothing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Pizza here is consistently good .']", "output": "[['Pizza', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .']", "output": "[['egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork', 'food quality', 'positive', 'love']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was very good , a great deal , and the place its self was great .']", "output": "[['food', 'food quality', 'positive', 'good'], ['food', 'food prices', 'positive', 'good'], ['place', 'ambience general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I couldn 't even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\"]", "output": "[['NULL', 'food style_options', 'negative', \"couldn 't even enjoy\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The Yellowtail was particularly good as well .']", "output": "[['Yellowtail', 'food quality', 'positive', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['With so many poor experiences to be had in the theater district , is truly an excellent find !']", "output": "[['NULL', 'restaurant general', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Price is high but the food is good , so I would come back again .']", "output": "[['food', 'food quality', 'positive', 'good'], ['food', 'food prices', 'negative', 'high'], ['NULL', 'restaurant general', 'positive', 'come back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A cool bar with great food , and tons of excellent beer .']", "output": "[['bar', 'ambience general', 'positive', 'cool'], ['food', 'food quality', 'positive', 'great'], ['beer', 'drinks quality', 'positive', 'excellent'], ['beer', 'drinks style_options', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The wine list was extensive - though the staff did not seem knowledgeable about wine pairings .']", "output": "[['wine list', 'drinks style_options', 'positive', 'extensive'], ['staff', 'service general', 'negative', 'not seem knowledgeable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Their calzones are horrific , bad , vomit-inducing , YUCK .']", "output": "[['calzones', 'food quality', 'negative', 'horrific'], ['calzones', 'food quality', 'negative', 'bad'], ['calzones', 'food quality', 'negative', 'vomit-inducing'], ['calzones', 'food quality', 'negative', 'YUCK']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Prices are in line .']", "output": "[['NULL', 'restaurant prices', 'neutral', 'in line']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I will definetly be going back .']", "output": "[['NULL', 'restaurant general', 'positive', 'going back']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We both opted for a pasta dish and they were served timely and fresh .']", "output": "[['NULL', 'service general', 'positive', 'served timely'], ['pasta dish', 'food quality', 'positive', 'fresh']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 've been many time and have never been disappointed .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'never been disappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['They have great rolls , the triple color and norwegetan rolls , are awesome and filling .']", "output": "[['rolls', 'food quality', 'positive', 'great'], ['triple color and norwegetan rolls', 'food quality', 'positive', 'awesome'], ['triple color and norwegetan rolls', 'food style_options', 'positive', 'filling']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The potato balls were not dry at all ... in fact it was buttery .']", "output": "[['potato balls', 'food quality', 'positive', 'not dry']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Very , very nice']", "output": "[['NULL', 'restaurant general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Have been several times and it never dissapoints .']", "output": "[['NULL', 'restaurant general', 'positive', 'never dissapoints']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\"]", "output": "[['seafood', 'food quality', 'positive', 'amazing'], ['wine list', 'drinks style_options', 'positive', 'good'], ['menu', 'food style_options', 'positive', 'ever-changing'], ['menu', 'food style_options', 'positive', 'great surprises']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Definitely a great spot for a nice occasion or date .']", "output": "[['spot', 'restaurant miscellaneous', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is excellent !']", "output": "[['food', 'food quality', 'positive', 'excellent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['One of the BEST']", "output": "[['NULL', 'restaurant general', 'positive', 'BEST']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We went around 9:30 on a Friday and it had died down a bit by then so the service was great !']", "output": "[['service', 'service general', 'positive', 'great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['This place is always packed .']", "output": "[['place', 'ambience general', 'neutral', 'packed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was good , the place was clean and affordable .']", "output": "[['food', 'food quality', 'positive', 'good'], ['place', 'ambience general', 'positive', 'clean'], ['place', 'restaurant prices', 'positive', 'affordable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['so delicious ! ! ! ! ! !']", "output": "[['NULL', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have known about this secret for the last 13 years , Emilio ( the Godfather ) has continued to serve food and wine for the gods at mortal prices .']", "output": "[['food', 'food quality', 'positive', 'gods'], ['wine', 'drinks quality', 'positive', 'gods'], ['food', 'food prices', 'positive', 'mortal'], ['wine', 'drinks prices', 'positive', 'mortal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Although the tables may be closely situated , the candle-light , food quality and service overcompensate .']", "output": "[['candle-light', 'ambience general', 'positive', 'overcompensate'], ['food', 'food quality', 'positive', 'overcompensate'], ['service', 'service general', 'positive', 'overcompensate'], ['tables', 'ambience general', 'negative', 'closely situated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Dessert is a joke ... dont bother']", "output": "[['Dessert', 'food quality', 'negative', 'joke']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Great staff .']", "output": "[['staff', 'service general', 'positive', 'Great']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is okay and the prices here are mediocre .']", "output": "[['food', 'food quality', 'neutral', 'okay'], ['NULL', 'restaurant prices', 'neutral', 'mediocre']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"At this point , the waitress comes over and asks us if everything was okay , I was literally so shocked that I was speechless and didn 't say anything , and guess what , the waitress WALKED away .\"]", "output": "[['waitress', 'service general', 'negative', 'speechless']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I have to say that I am pleasantly suprised and I will most likely stop in again if I am in the neighborhood .']", "output": "[['NULL', 'restaurant general', 'positive', 'pleasantly suprised']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Loved It']", "output": "[['NULL', 'restaurant general', 'positive', 'Loved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"If celebrities make you sweat , then your in for a ride , but if your like most around these parts then you 'll just yawn and wonder whats with all the hype .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'yawn']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['A little crowded but they move that line really fast !']", "output": "[['NULL', 'service general', 'positive', 'fast'], ['NULL', 'restaurant miscellaneous', 'negative', 'crowded']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Gross food \u2013 Wow-']", "output": "[['food', 'food quality', 'negative', 'Gross']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .']", "output": "[['portions', 'food style_options', 'neutral', 'HUGE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Try the Pad Thai , it 's fabulous and their prices are so cheap !\"]", "output": "[['Pad Thai', 'food quality', 'positive', 'Try'], ['Pad Thai', 'food quality', 'positive', 'fabulous'], ['NULL', 'restaurant prices', 'positive', 'cheap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My husband and I thougt it would be great to go to the Jekyll and Hyde Pub for our anniversary , and to our surprise it was fantastic .']", "output": "[['Jekyll and Hyde Pub', 'restaurant general', 'positive', 'great'], ['Jekyll and Hyde Pub', 'restaurant general', 'positive', 'fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"i know , you were too busy showing off your vintage tee shirt and looking bored , but my agenda is i 'm here to eat and enjoy the company of friends , seeking a pleasant experience .\"]", "output": "[['NULL', 'service general', 'negative', 'bored']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['My chow fun and chow see was really bland and oily .']", "output": "[['chow fun and chow see', 'food quality', 'negative', 'bland'], ['chow fun and chow see', 'food quality', 'negative', 'oily']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Admittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .']", "output": "[['open kitchen', 'ambience general', 'positive', 'charm'], ['restaurant', 'ambience general', 'negative', 'warm']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I recommend this place to everyone .']", "output": "[['place', 'restaurant general', 'positive', 'recommend']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We had half/half pizza , mine was eggplant and my friend had the buffalo and it was sooo huge for a small size pizza !']", "output": "[['half/half pizza', 'food style_options', 'positive', 'huge']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"please do n't fool us .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'fool']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is tasty and portion sizes are appropriate .']", "output": "[['food', 'food quality', 'positive', 'tasty'], ['portion sizes', 'food style_options', 'positive', 'appropriate']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['lobster was good , nothing spectacular .']", "output": "[['lobster', 'food quality', 'neutral', 'good']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Chennai Garden is my favorite Indian restaurant in the city .']", "output": "[['Chennai Garden', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Beautiful experience .']", "output": "[['NULL', 'restaurant general', 'positive', 'Beautiful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Do n't get me started on the margaritas , either .\"]", "output": "[['margaritas', 'drinks quality', 'negative', \"Do n't get me started\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Wretched and retching']", "output": "[['NULL', 'restaurant general', 'negative', 'Wretched'], ['NULL', 'restaurant general', 'negative', 'retching']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food is delicious - from the specials to the regular menu-fare , the dishes are never a disappointment .']", "output": "[['food', 'food quality', 'positive', 'delicious'], ['dishes', 'food quality', 'positive', 'never a disappointment'], ['specials', 'food quality', 'positive', 'delicious'], ['regular menu-fare', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Not because you are `` The Four Seasons `` ... \u2013 you are allowed to charge an arm and a leg for a romatic dinner .']", "output": "[['The Four Seasons', 'restaurant prices', 'negative', 'charge an arm and a leg']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"The high prices you 're going to pay is for the view not for the food .\"]", "output": "[['NULL', 'restaurant prices', 'negative', 'high']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Located at the end of a magnificent block .']", "output": "[['NULL', 'location general', 'positive', 'magnificent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The staff is incredibly helpful and attentive .']", "output": "[['staff', 'service general', 'positive', 'helpful'], ['staff', 'service general', 'positive', 'attentive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['It was well worth the wait .']", "output": "[['NULL', 'restaurant general', 'positive', 'worth']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Taxan delicious !']", "output": "[['Taxan', 'food quality', 'positive', 'delicious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's boring on the inside , and our sushi was pretty below average ... the tuna was soggy and the other rolls had no flavor .\"]", "output": "[['NULL', 'ambience general', 'negative', 'boring'], ['sushi', 'food quality', 'negative', 'below average'], ['tuna', 'food quality', 'negative', 'soggy'], ['rolls', 'food quality', 'negative', 'no flavor']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Be prepared to wait , because the place is pretty tiny .']", "output": "[['place', 'restaurant miscellaneous', 'negative', 'tiny'], ['place', 'ambience general', 'negative', 'tiny']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Fantastic place .']", "output": "[['place', 'restaurant general', 'positive', 'Fantastic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"It 's definately not a place to go if you want to impress someone .\"]", "output": "[['place', 'restaurant miscellaneous', 'negative', 'impress']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"However , once I received my predictably mediocre order of what Dokebi thinks passes as Korean fair , ( sometimes you have to settle when it 's your only option ) , I got through about half my kimchee before I found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\"]", "output": "[['kimchee', 'food quality', 'negative', 'disgusting'], ['Korean fair', 'food quality', 'negative', 'mediocre']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I am happy i did the food was awsome .']", "output": "[['food', 'food quality', 'positive', 'awsome']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Amma is nothing special .']", "output": "[['Amma', 'restaurant general', 'neutral', 'nothing special']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Calling the place Hampton Chutney Co. does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .']", "output": "[['place', 'restaurant general', 'negative', 'unattractive'], ['room', 'ambience general', 'negative', 'unattractive'], ['clerks', 'service general', 'negative', 'unhelpful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['THE BIG COMPLAINT : NO TOASTING AVAILABLE .']", "output": "[['NULL', 'service general', 'negative', 'COMPLAINT']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .']", "output": "[['brioche and lollies', 'food quality', 'positive', 'cute'], ['brioche and lollies', 'food quality', 'positive', 'sweet']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I lOVE THIS PLACE !']", "output": "[['PLACE', 'restaurant general', 'positive', 'lOVE']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Thanks Bloom 's for a lovely trip .\"]", "output": "[[\"Bloom 's\", 'restaurant general', 'positive', 'Thanks'], [\"Bloom 's\", 'restaurant general', 'positive', 'lovely']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['We had a very nice time .']", "output": "[['NULL', 'restaurant general', 'positive', 'nice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Going to Bark is always worth the train ride , and will make your tongue and belly very happy !']", "output": "[['Bark', 'restaurant general', 'positive', 'worth'], ['NULL', 'food quality', 'positive', 'happy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['All in all , I would return - as it was a beautiful restaurant - but I hope the staff pays more attention to the little details in the future .']", "output": "[['restaurant', 'restaurant general', 'positive', 'beautiful'], ['restaurant', 'ambience general', 'positive', 'beautiful'], ['staff', 'service general', 'negative', 'pays more attention']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Authentic Taiwanese food that 's cheap ... what more could you ask for ?\"]", "output": "[['Taiwanese food', 'food quality', 'positive', 'Authentic'], ['Taiwanese food', 'food prices', 'positive', 'cheap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I 'm not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and I all enjoy Mizu very much ... and we 're repeat customers .\"]", "output": "[['Mizu', 'restaurant general', 'positive', 'enjoy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['Of course , it is crowded but who cares .']", "output": "[['NULL', 'ambience general', 'neutral', 'crowded but who cares']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\"]", "output": "[['food', 'food quality', 'positive', 'recommend'], ['ambience', 'ambience general', 'positive', 'recommend'], ['service', 'service general', 'positive', 'recommend'], ['service', 'service general', 'positive', 'wonderful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['One of my favorite places in Manhattan .']", "output": "[['NULL', 'restaurant general', 'positive', 'favorite']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The food was excellent as well as service , however , I left The Four Seasons very dissappointed .']", "output": "[['food', 'food quality', 'positive', 'excellent'], ['service', 'service general', 'positive', 'excellent'], ['The Four Seasons', 'restaurant general', 'negative', 'dissappointed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "[\"Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\"]", "output": "[['sake list', 'drinks style_options', 'positive', 'extensive'], ['NULL', 'service general', 'positive', 'made for us upon request']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .']", "output": "[['in-house lady DJ', 'ambience general', 'positive', 'good taste']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['you know what i mean all the positives things happening there made mw write this review .']", "output": "[['NULL', 'restaurant general', 'positive', 'positives']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}
{"task_type": "generation", "dataset": "absa-quad", "input": "['I had the duck breast special on my last visit and it was incredible .']", "output": "[['duck breast special', 'food quality', 'positive', 'incredible']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example:  Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "}