{"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": "['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": "['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": "[\"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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "[\"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": "['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": "['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": "[\"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": "['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 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": "[\"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": "[\"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": "['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": "['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": "[\"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": "[\"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": "['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": "['Inexpensive , unassuming , great time !']", "output": "[['NULL', 'restaurant prices', 'positive', 'Inexpensive'], ['NULL', 'ambience general', 'positive', 'unassuming'], ['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": "['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": "['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": "['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": "['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": "[\"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": "['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": "['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": "[\"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": "['None was made so i hung up .']", "output": "[['NULL', 'service general', 'negative', 'None was made']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "['The food is great and the environment is even better .']", "output": "[['food', 'food quality', 'positive', 'great'], ['environment', 'ambience 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": "['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 '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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "[\"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": "['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": "[\"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": "['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": "['The chicken lollipop is my favorite , most of the dishes ( I have to agree with a previous reviewer ) are quite oily and very spicy , espeically the Chilli Chicken .']", "output": "[['chicken lollipop', 'food quality', 'positive', 'favorite'], ['dishes', 'food quality', 'negative', 'oily'], ['dishes', 'food quality', 'negative', 'spicy'], ['Chilli Chicken', 'food quality', 'negative', 'oily'], ['Chilli Chicken', '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": "['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": "['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": "[\"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": "['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": "['The stuff tilapia was horrid ... tasted like cardboard .']", "output": "[['stuff tilapia', 'food quality', '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 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": "[\"Our waitress was n't mean , but not especially warm or attentive either .\"]", "output": "[['waitress', 'service general', 'neutral', \"was n't mean\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "[\"Regardless , we 'll be back and can 't wait to visit in the summer to take advantage of the patio .\"]", "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": "['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": "['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": "['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": "[\"But for the Shabu Shabu , you won 't find much better in NY .\"]", "output": "[['Shabu Shabu', 'food quality', 'positive', \"won 't find much 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 'm a friendly person , so I wouldn 't mind had she not been so nasty and gotten so personal .\"]", "output": "[['NULL', 'service general', 'negative', 'nasty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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": "['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": "['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": "['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": "['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": "['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": "['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": "['My party had the BBE $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !']", "output": "[['BBE $ 29 fixe prix menu', 'food prices', 'positive', 'wonderful'], ['BBE $ 29 fixe prix menu', 'food style_options', 'positive', 'a flight of sake']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "['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": "['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": "['Paul , the maitre, 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": "['They honored reservation on Sunday afternoon very well .']", "output": "[['NULL', 'service general', 'positive', 'very 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 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": "['The food is very good , but not outstanding .']", "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": "['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": "['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": "[\"Easily the worst stir-fried squid I 've ever tasted .\"]", "output": "[['stir-fried squid', '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": "[\"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 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 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": "['I had the Thai style Fried Sea Bass ... which was very good .']", "output": "[['Thai style Fried Sea Bass', '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 don \u2019 t usually visit the same establishment more than once , what more twice , but I \u2019 ll come to Zenkichi anytime for a quiet , unhurried and memorable dinner .']", "output": "[['Zenkichi', 'restaurant general', 'positive', 'come to Zenkichi anytime'], ['Zenkichi', 'ambience general', 'positive', 'quiet'], ['Zenkichi', 'ambience general', 'positive', 'unhurried'], ['Zenkichi', 'ambience general', 'positive', '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": "['Red Dragon Roll - my favorite thing to eat , of any food group - hands down']", "output": "[['Red Dragon Roll', '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": "['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": "['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": "['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": "['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": "['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": "[\"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": "['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": "['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": "['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": "['And $ 11 for a plate of bland guacamole ?']", "output": "[['guacamole', 'food quality', 'negative', 'bland'], ['guacamole', 'food prices', '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 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": "['Probably would not go back here .']", "output": "[['NULL', 'restaurant general', 'negative', 'would 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": "['The hostess was very pleasant .']", "output": "[['hostess', 'service 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": "['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": "['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": "['This is undoubtedly my favorite modern Japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !']", "output": "[['modern Japanese brasserie', 'restaurant general', 'positive', 'favorite'], ['modern Japanese brasserie', '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 bread we received was horrible - rock hard and cold - and the `` free `` appetizer of olives was disappointing .']", "output": "[['bread', 'food quality', 'negative', 'horrible'], ['appetizer of olives', 'food quality', 'negative', 'disappointing'], ['appetizer of olives', 'food prices', 'neutral', '`` 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": "['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": "['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": "['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": "['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": "['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": "['Best In ALL of NYC']", "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": "['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": "['$ 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": "['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": "['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": "['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": "['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": "['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": "['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": "['The have a great cocktail with Citrus Vodka and lemon and lime juice and mint leaves that is to die for !']", "output": "[['cocktail with Citrus Vodka and lemon and lime juice and mint leaves', '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": "['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": "['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": "['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": "['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 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": "['We did arrive late for our reservation so I can not complain too much about the wait for a table .']", "output": "[['wait', 'service general', 'neutral', 'can not complain too 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 place doesn 't make any sense\"]", "output": "[['place', 'restaurant general', 'negative', \"doesn 't make any sense\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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 spinach is fresh , definately not frozen ...']", "output": "[['spinach', '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": "['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": "['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": "['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": "['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": "['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 concept of japanese tapas is newly created and clearly does n't work .\"]", "output": "[['japanese tapas', 'food style_options', 'negative', \"does n't work\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "[\"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": "['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": "['Terrible Waste of money . . scammers']", "output": "[['NULL', 'restaurant general', 'negative', 'Terrible'], ['NULL', 'restaurant prices', 'negative', 'Waste of 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": "[\"This is where it really really gets bad : the manager said , there is absolutely nothing we can do , it 's a matter of taste that she did n't like it , and I can not comp it .\"]", "output": "[['manager', '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": "['One of my favorite places in Brooklyn .']", "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": "['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": "['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": "['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": "['Lexicographers take note : a new and fascinating definition of rudeness is alive and flourishing right here in Brooklyn .']", "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": "['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": "[\"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 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": "['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": "['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": "['Will prob . not return but it is a great dinning experience to try atleast once .']", "output": "[['NULL', 'restaurant general', 'negative', 'not return'], ['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": "['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": "[\"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": "['The nakgi-bokum was horrible .']", "output": "[['nakgi-bokum', 'food quality', '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": "['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": "['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": "['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": "['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": "['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": "['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": "['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 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": "['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": "['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": "['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": "['Poor service and management']", "output": "[['service', 'service general', 'negative', 'Poor'], ['management', '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": "['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": "['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": "['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": "['Our visit their to say the least , was an unpleasant and costly 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": "['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": "['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": "['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": "['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": "['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": "['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": "[\"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": "['Service was wonderful ;']", "output": "[['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": "['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": "['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": "['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": "['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": "[\"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": "[\"Just want to warn you all - do n't waste your time and money .\"]", "output": "[['NULL', 'restaurant general', 'negative', 'waste']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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": "['I was shocked that my friends wanted to stay after the waitress said , `` can I help you `` and `` how many are in your party . ``']", "output": "[['waitress', 'service general', 'negative', 'shocked']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "['Subtle food and service']", "output": "[['food', 'food quality', 'positive', 'Subtle'], ['service', 'service general', '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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "[\"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": "['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": "['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 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": "[\"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": "['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": "['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": "['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": "['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": "[\"I was n't here for the pizza so I can 't comment on that yet but what I had was very good .\"]", "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": "[\"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": "[\"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": "['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": "['Mussles and calamari were superb Saturday evening .']", "output": "[['Mussles', 'food quality', 'positive', 'superb'], ['calamari', '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": "['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": "[\"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": "['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": "['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": "['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": "['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": "[\"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": "['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 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": "[\"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": "['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": "['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": "[\"But $ 500 for a dinner for two that didn 't include Wine ?\"]", "output": "[['dinner for two', 'food prices', 'negative', \"didn 't include Wine\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "[\"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": "['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": "['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": "['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": "['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": "[\"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": "['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": "[\"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": "['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": "['Expensive']", "output": "[['NULL', 'restaurant 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": "[\"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": "['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": "[\"I honestly do n't even know where to begin .\"]", "output": "[['NULL', 'restaurant general', 'negative', \"do n't even know where to begin\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .']", "output": "[['food', 'food quality', 'negative', '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": "['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": "['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": "[\"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": "['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": "[\"The food we ordered was excellent , although I would n't say the margaritas were anything to write home about .\"]", "output": "[['food', 'food quality', 'positive', 'excellent'], ['margaritas', 'drinks quality', 'neutral', 'anything to write home about']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "[\"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": "['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": "['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": "['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": "['The sauce tasted more like Chinese fast food than decent Korean .']", "output": "[['sauce', 'food quality', 'negative', 'Chinese fast 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": "['overpriced japanese food with mediocre service']", "output": "[['japanese food', 'food prices', 'negative', 'overpriced'], ['service', 'service general', '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": "['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": "['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": "['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": "['its just a fun place to go , not a five star restaraunt .']", "output": "[['restaraunt', 'restaurant general', 'neutral', '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": "['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": "['Food took some time to prepare , all worth waiting for .']", "output": "[['Food', 'food quality', 'positive', 'worth'], ['NULL', 'service general', 'neutral', 'took some 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": "['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": "['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": "['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": "['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": "['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": "[\"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 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 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": "['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": "['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": "['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": "['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": "[\"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": "[\"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": "['However , our $ 14 drinks were were horrible !']", "output": "[['drinks', 'drinks quality', 'negative', 'horrible'], ['drinks', 'drinks prices', '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": "['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": "[\"Each time we 've been , the front of house staff ( not the waiters - they 're fantastic - but the people who greet and seat you ) has been so hideous to us that were it not for the exceptional fish dishes I would never return .\"]", "output": "[['waiters', 'service general', 'positive', 'fantastic'], ['front of house staff', 'service general', 'negative', 'hideous'], ['fish dishes', '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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['I literally just got back home after visiting Casa La Femme and was so offended by my visit felt it necessary to try and warn other diners who value their money and time .']", "output": "[['Casa La Femme', 'restaurant general', 'negative', 'offended'], ['Casa La Femme', 'restaurant prices', 'negative', 'offended']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "['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": "['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": "['Overpriced and not tasty']", "output": "[['NULL', 'food prices', 'negative', 'Overpriced'], ['NULL', 'food quality', 'negative', 'not 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 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": "['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": "['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": "['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": "['Best . Sushi . Ever .']", "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": "['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": "['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": "['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": "['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": "['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 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": "['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": "['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": "['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": "['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 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": "['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": "['For each course we waited over 1 /2 hour to 45 minutes and were never offered a drink .']", "output": "[['NULL', 'service general', 'negative', 'waited over 1 /2 hour to 45 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": "['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": "['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": "[\"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": "['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": "['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": "['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": "['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": "['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 '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": "['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": "['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": "['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": "['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": "['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": "['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": "['Sushi experience was unbelievable with my fiance .']", "output": "[['Sushi', 'food quality', 'positive', 'unbelievable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "[\"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": "['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": "['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": "['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": "['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": "['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": "['One special roll and one regular roll is enough to fill you up , but save room for dessert !']", "output": "[['dessert', 'food quality', 'positive', 'save room'], ['special roll', 'food style_options', 'positive', 'enough'], ['regular roll', 'food 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": "['On our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .']", "output": "[['NULL', 'service general', 'negative', 'waiting an extra 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": "['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": "['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": "['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": "[\"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": "['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": "['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": "['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": "['The Cypriot restaurant has a lot going for it .']", "output": "[['Cypriot restaurant', 'restaurant general', 'positive', 'a lot going']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "[\"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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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 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": "['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": "['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": "['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": "['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": "[\"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": "['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": "[\"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": "['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 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": "['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": "['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": "['No desert menu , 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": "[\"Fabulous food - if the front of house staff do n't put you off \u2013\"]", "output": "[['food', 'food quality', 'positive', 'Fabulous'], ['front of house staff', 'service general', 'negative', 'put you 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": "['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": "['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": "['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": "['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": "['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 '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": "['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": "['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": "['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": "[\"Can 't argue about that , but they are clearly over priced .\"]", "output": "[['NULL', 'food 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": "['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": "['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": "['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": "['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": "['I asked for a menu and the same waitress looked at my like I was insane .']", "output": "[['waitress', 'service general', 'negative', 'insane']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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": "[\"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 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": "['At first we were a little taken aback , as this seemed to present a problem , although the restaurant looked fairly empty , but they hastily put the table together for us .']", "output": "[['NULL', 'service general', 'positive', 'hastily put the table together for us']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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": "['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": "['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": "['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": "[\"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": "['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": "['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": "['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": "['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": "['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": "[\"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": "[\"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": "['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": "[\"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": "['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": "['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": "['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": "['Maitre-D- `` Eat and get out ``']", "output": "[['Maitre-D', 'service general', 'negative', 'get 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": "['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": "['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": "['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": "['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": "['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 can 't remember the last time I had such gross food in New York .\"]", "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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['The bar drinks were Eh , ok to say the least .']", "output": "[['bar drinks', 'drinks 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": "['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": "[\"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": "['My father had the flank steak which was very good , and my mother had the swordfish .']", "output": "[['flank steak', '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 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": "['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": "['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": "['I was speechless by the horrible food .']", "output": "[['food', 'food quality', '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": "['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": "['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": "['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": "['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": "['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": "['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": "[\"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": "['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": "['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": "['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": "['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": "['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": "['We had fun eating in there , we were there like around 3 a .m . in the morning !']", "output": "[['NULL', 'restaurant general', 'positive', 'had 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": "['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": "['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": "['Best meal in a long time !']", "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": "['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": "['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": "['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": "['Vanison was good but not amazing .']", "output": "[['Vanison', '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": "['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": "['Service ok but unfriendly , filthy bathroom .']", "output": "[['Service', 'service general', 'negative', 'unfriendly'], ['bathroom', 'ambience general', 'negative', 'filthy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 be refunded !']", "output": "[['NULL', 'restaurant general', 'negative', 'refunded']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "['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": "['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 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": "['What we did do was waste 3 hours being trapped in a table waiting and waiting for food and drinks and hooka . . some of which we never received !']", "output": "[['NULL', 'service general', 'negative', 'never 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": "['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": "['Excellent sashimi , and the millennium roll is beyond delicious .']", "output": "[['sashimi', 'food quality', 'positive', 'Excellent'], ['millennium roll', '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 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": "['Restaurant with a view']", "output": "[['view', 'location general', 'neutral', 'with a view']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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": "['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": "['Although we were told 10-15 minutes and it was more like 45 minutes .']", "output": "[['NULL', 'service general', 'negative', '45 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 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": "['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": "['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": "['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": "['There is something about their atmosphere that makes me come back nearly every week .']", "output": "[['atmosphere', 'ambience 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": "['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": "['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": "['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": "['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": "['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": "['What a hassle !']", "output": "[['NULL', 'restaurant general', 'negative', 'hassle']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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": "['Not a very fancy place but very good Chinese style Indian food .']", "output": "[['place', 'ambience general', 'neutral', 'fancy'], ['Chinese style Indian 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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['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": "['We asked for beverages and never received them .']", "output": "[['NULL', 'service general', 'negative', 'never 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": "['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": "['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": "['bad staff']", "output": "[['staff', '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": "[\"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": "[\"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 side dishes were passable , and I did get a refill upon request .']", "output": "[['side dishes', 'food quality', 'neutral', 'passable'], ['side dishes', 'food style_options', 'positive', 'refill']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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": "['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": "['the food is always fresh ...']", "output": "[['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": "['But nonetheless -- great spot , great food .']", "output": "[['spot', 'restaurant 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": "['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": "['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": "['So 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": "['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": "['Possibly the Most Romantic Restaurant in the City']", "output": "[['Restaurant', '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": "['Oh , and I never write reviews -- I just was so moved by how bad this place was , I felt it was my duty to spread the word .']", "output": "[['place', 'restaurant 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": "['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": "['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": "['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": "['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": "['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": "['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": "['Everything was going good until we got our meals .']", "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": "['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": "['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": "[\"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": "['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": "['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": "['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": "['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 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": "['Delicious , creative and fun .']", "output": "[['NULL', 'food quality', 'positive', 'Delicious'], ['NULL', 'food style_options', 'positive', 'creative'], ['NULL', 'food style_options', '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 downside ... they only take cash which is OK if you know about it ahead of time .']", "output": "[['NULL', 'restaurant miscellaneous', '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": "['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": "['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": "['I waited for 10-15 minutes for service ordered a beer & was never served again .']", "output": "[['service', 'service general', 'negative', 'never served 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 ambiance was a peaceful and relaxing break amongst all the kids running around in Downtown Disney .']", "output": "[['ambiance', '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": "['hidden little jem']", "output": "[['NULL', 'restaurant general', 'positive', 'hidden']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 with a friend from out of town ... and we were both very 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": "['It has great sushi and even better service .']", "output": "[['sushi', 'food quality', 'positive', 'great'], ['service', 'service 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": "['All their menu items are a hit , and they serve mimosas .']", "output": "[['menu items', 'food quality', '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": "['\u2013 ... and the best summertime deck experience -- they will even bring you a blanket if you get cold in the Seattle evening weather .']", "output": "[['deck', 'ambience general', 'positive', 'best'], ['NULL', 'service general', 'positive', 'bring you a blanket']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "[\"\u2013 After 12 years in Seattle Ray 's rates as the place we always go back to .\"]", "output": "[[\"Ray 's\", '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": "[\"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 think I have probably tried each item on their menu at least once it is all excellent .']", "output": "[['menu', '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": "[\"\u2013 The pepperoni 's cut real thick -- Yum .\"]", "output": "[[\"pepperoni 's\", 'food style_options', 'positive', 'thick']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Green tea creme brulee gets better each time I have it .']", "output": "[['Green tea creme brulee', '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": "['not too fine thought that you feel uncomfortable and have to dress up .']", "output": "[['NULL', 'ambience 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": "['Atmosphere was nice .']", "output": "[['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": "['absolutely fabulous ! ! !']", "output": "[['NULL', 'restaurant general', '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": "['AVOID THE 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": "[\"To be honest , I 've had better frozen pizza .\"]", "output": "[['pizza', 'food quality', 'negative', '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 service was exceptional - sometime there was a feeling that we were served by the army of friendly waiters .']", "output": "[['service', 'service general', 'positive', 'exceptional'], ['waiters', '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": "[\"And they packaged everything nicely so it did n't spill .\"]", "output": "[['NULL', 'service general', 'positive', 'nicely']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 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": "['The food is sinful .']", "output": "[['food', 'food quality', 'positive', 'sinful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 start off , approximately 8-10 oz of orange juice will cost you $ 3 .']", "output": "[['orange juice', 'drinks prices', 'negative', 'cost you $ 3']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 sake selection .']", "output": "[['sake selection', 'drinks 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": "['While I could have done without the youth who shared the evening with us , our wonderful server and food made the experience a very positive one .']", "output": "[['server', 'service general', 'positive', 'wonderful'], ['food', 'food quality', 'positive', 'wonderful'], ['NULL', 'restaurant miscellaneous', 'positive', 'positive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 find !']", "output": "[['NULL', 'restaurant general', 'positive', 'What a find']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['\u2013 Mioposto has a very creative & delicious pizza menu .']", "output": "[['pizza menu', 'food quality', 'positive', 'delicious'], ['pizza menu', 'food style_options', 'positive', '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": "['On a recent trip , our waiter was extremely dismissive , while no less than three staff members waited hand-and-foot on a pair of Japanese girls seated nearby .']", "output": "[['waiter', 'service general', 'negative', 'dismissive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 normally not finish the brocolli when i order these kinds of food but for the first time , every piece was as eventful as the first one ... the scallops and prawns was so fresh and nicely cooked .']", "output": "[['scallops', 'food quality', 'positive', 'fresh'], ['scallops', 'food quality', 'positive', 'nicely cooked'], ['prawns', 'food quality', 'positive', 'fresh'], ['prawns', 'food quality', 'positive', 'nicely cooked'], ['brocolli', 'food quality', 'positive', 'eventful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 n't the cheapest sushi but has been worth it every time .\"]", "output": "[['sushi', 'food prices', 'neutral', \"is n't the cheapest\"], ['sushi', '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": "['You never feel icky and stuffed after you eat there .']", "output": "[['NULL', 'food quality', 'positive', 'never feel icky and stuffed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 part of a small party of four , our food was dropped off without comment ;']", "output": "[['NULL', 'service general', 'negative', 'dropped off without comment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['I hope one day Scooner or Later returns to what it once was .']", "output": "[['Scooner or Later', 'restaurant general', 'negative', 'returns to what it once was']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 off , the food came fast and all together : ) i like that ... i hate the pretentiousness of things coming in one after the other .']", "output": "[['NULL', 'service 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 food was 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": "['If there is a line very day of the week for the entire time a place is open , you know it 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 most pleasant surprise was the check that did not exceed my expectations as it always happens in most of the places .']", "output": "[['NULL', 'restaurant prices', '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": "['At best , the food was good and definately overpriced .']", "output": "[['food', 'food quality', 'positive', 'good'], ['food', '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 outside patio area has an abbreviated menu .']", "output": "[['menu', 'food style_options', 'neutral', 'abbreviated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 food in L.A .']", "output": "[['Indian 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": "['Cut to the chase - this is amazing !']", "output": "[['NULL', 'restaurant 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": "['The food is not what it once was ( potions have seriously seen downsizing ) prices have gone up , and the service is the worst I have experienced anywhere ( including mainland Europe ) .']", "output": "[['food', 'food quality', 'negative', 'not what it once was'], ['potions', 'food style_options', 'negative', 'downsizing'], ['service', 'service general', 'negative', 'worst'], ['food', 'food prices', 'negative', 'gone 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": "['Lebanese Food ! Yum !']", "output": "[['Lebanese Food', 'food quality', 'positive', 'Yum']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['the brocollis were so fresh and tasty .']", "output": "[['brocollis', '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": "['And they give good quantity for the price .']", "output": "[['NULL', 'food style_options', 'positive', 'good quantity']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 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": "[\"Seattle 's BEST Winelist\"]", "output": "[['Winelist', 'drinks 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": "[\"\u2013 I ca n't say enough about this place .\"]", "output": "[['place', 'restaurant general', 'positive', \"ca n't say 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": "['\u2013 i have been eating at this place for over 8 years now and i have never had one bad meal .']", "output": "[['meal', 'food quality', '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": "[\"I went home & looked them up online again where I discovered there is a link for a give away that doesn 't work so emailed the restaurant about the non existent service & deceptive link .\"]", "output": "[['restaurant', 'restaurant miscellaneous', 'negative', 'non existent'], ['restaurant', 'restaurant miscellaneous', 'negative', 'deceptive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Fancy pieces of exotic fish on a $ 100 dollar plate and NOT ONE was eatable .']", "output": "[['plate', 'food prices', 'negative', ' 100 dollar'], ['exotic fish', 'food quality', 'negative', 'NOT ONE was eatable'], ['exotic fish', 'food style_options', 'negative', 'Fancy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 yummy lamb korma , saag paneer , samosas , naan , etc .']", "output": "[['lamb korma', 'food quality', 'positive', 'yummy'], ['saag paneer', 'food quality', 'positive', 'yummy'], ['samosas', 'food quality', 'positive', 'yummy'], ['naan', '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": "['Serves really good sushi .']", "output": "[['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": "[\"As usual the omikase did n't disappoint in freshness , although it scored low on creativity and selection .\"]", "output": "[['omikase', 'food quality', 'positive', \"did n't disappoint in freshness\"], ['omikase', 'food style_options', 'negative', 'scored low on creativity and 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": "[\"Rice is too dry , tuna was n't so fresh either .\"]", "output": "[['Rice', 'food quality', 'negative', 'too dry'], ['tuna', 'food quality', 'negative', \"was n't so 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": "['Their crab eggs benedict is addicting .']", "output": "[['crab eggs benedict', 'food quality', 'positive', 'addicting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 room for scrumptious desserts .']", "output": "[['desserts', 'food quality', 'positive', 'scrumptious']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 professional and friendly .']", "output": "[['server', '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": "['I went here on a recommendation and will surely return time and time again .']", "output": "[['NULL', 'restaurant general', 'positive', 'recommendation']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 all good but it was way too mild .']", "output": "[['food', 'food quality', 'positive', 'good'], ['food', 'food quality', 'negative', 'too mild']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['\u2013 My husband and I love eating at Mioposto Caf\u00e9 .']", "output": "[['Mioposto Caf\u00e9', '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": "['\u2013 This was great dining 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": "['The staff was really friendly .']", "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": "['We easily spent more than $ 40 per person ( not including alcohol ) and were still hungry .']", "output": "[['NULL', 'restaurant prices', 'negative', 'more than $ 40'], ['NULL', 'food style_options', 'negative', 'hungry']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 right up there with places in Tokyo as far as the freshness is concerned .']", "output": "[['NULL', '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": "['Groovy music made the dinner casual .']", "output": "[['music', 'ambience general', 'positive', '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": "['After one member of our party had been bumped repeatedly by a waitress , a polite request that he not be bumped sent the waitress into an abusive rant .']", "output": "[['waitress', 'service general', 'negative', 'an abusive rant']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "[\"\u2013 Ray 's ( suprisingly ) has the city 's BEST & most diverse wine list .\"]", "output": "[['wine list', 'drinks 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": "[\"I probably wouldn 't go back though 'cuz I do n't know if it 's worth it .\"]", "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 chips and salsa are so yummy , and the prices are fabulous .']", "output": "[['chips and salsa', 'food quality', 'positive', 'yummy'], ['NULL', 'restaurant prices', '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": "['The sommelier is fantastic , down-to-earth , & extremely knowlegable .']", "output": "[['sommelier', 'service general', 'positive', 'fantastic'], ['sommelier', '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": "['big thick pepperoni']", "output": "[['pepperoni', 'food style_options', 'positive', 'big thick']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 worth going at all and spend your money there ! ! !']", "output": "[['NULL', 'restaurant general', '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": "['Insultingly Overpriced']", "output": "[['NULL', 'restaurant 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": "['Indoor ambience was modern .']", "output": "[['Indoor ambience', 'ambience general', 'positive', 'modern']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['DONOT GO !']", "output": "[['NULL', 'restaurant general', 'negative', 'DONOT 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": "['Untill this happens , my advice is to 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": "[\"Oh I forgot to mention that they do n't have bread service .\"]", "output": "[['NULL', 'service general', 'negative', \"do n't have bread service\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 greasy or anything .']", "output": "[['NULL', 'food quality', 'positive', 'not greasy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['The music is great , no night better or worse , the bar tenders are generous with the pouring , and the lighthearted atmosphere will lifts you spirits .']", "output": "[['music', 'ambience general', 'positive', 'great'], ['bar tenders', 'drinks style_options', 'positive', 'generous'], ['atmosphere', 'ambience general', 'positive', 'lighthearted']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 ambience , but highly overrated place .']", "output": "[['ambience', 'ambience general', 'positive', 'Nice'], ['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": "['The sushi here is delicious !']", "output": "[['sushi', '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": "[\"\u2013 I am exceedingly pleased to report that my dinner at Ray 's Boathouse last Friday completely exceeded my expectations .\"]", "output": "[[\"Ray 's Boathouse\", 'restaurant general', 'positive', 'exceeded my 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": "['Dinners have always been excellent , in terms of food quality .']", "output": "[['Dinners', '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": "['Their apps are all delicious .']", "output": "[['apps', '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": "[\"Blue Ribbon lives up to it 's fantastic reputation .\"]", "output": "[['Blue Ribbon', '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 felt ackward and next time went to the casino bathroom .']", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'ackward']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['\u2013 I decided to eat at Stack because of their price fixed pre-show dinner .']", "output": "[['price fixed pre-show dinner', 'food prices', 'neutral', 'fixed']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['\u2013 I took my parents here for their anniversary-very very 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": "['Unbeatable sushi !']", "output": "[['sushi', 'food quality', 'positive', 'Unbeatable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 start things off , our lovely server Brooke was quickly on hand to take my drink order .']", "output": "[['Brooke', 'service 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": "['Every time `` 0-sixtynine `` is called the bartender buys everyone drinks !']", "output": "[['bartender', 'service general', 'positive', 'buys everyone drinks']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Endless fun , awesome music , great staff ! ! !']", "output": "[['music', 'ambience general', 'positive', 'awesome'], ['staff', 'service general', 'positive', 'great'], ['NULL', '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": "['Fair menu selection .']", "output": "[['menu selection', 'food style_options', 'neutral', '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": "['Two rascally kids were seated near us for the first part of our dinner ... they were replaced by a delightful preteen who pretended to gag every time seafood was mentioned at her table .']", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'rascally']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['I rejected it because in the process of attempting to fix the eggs they broke something else in the dish and I was too frustrated to continue .']", "output": "[['dish', 'food quality', 'negative', 'rejected']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 many dishes but the BEST was the lobster 3 ways .']", "output": "[['lobster 3 ways', '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 appetizer was interesting , but the Creme Brulee was very savory and delicious .']", "output": "[['appetizer', 'food quality', 'positive', 'interesting'], ['Creme Brulee', 'food quality', 'positive', 'savory'], ['Creme Brulee', '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 decor was beautiful and unique .']", "output": "[['decor', '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": "[\"It 's fresh , welcoming , delicious , and relaxing .\"]", "output": "[['NULL', 'food quality', 'positive', 'fresh'], ['NULL', 'food quality', 'positive', 'delicious'], ['NULL', 'ambience general', 'positive', 'welcoming'], ['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": "['Addicting !']", "output": "[['NULL', 'restaurant general', 'positive', 'Addicting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 chickpeas , which I normally find too dry , were good .']", "output": "[['chickpeas', '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": "['This place rocks ! !']", "output": "[['place', 'restaurant 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": "[\"That 's a huge compliment , especially in the fickled restaurant business , ... enough already !\"]", "output": "[['NULL', 'restaurant general', 'positive', 'huge compliment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 sitting there with my empty glass for over 20 minutes I left .']", "output": "[['NULL', 'service general', 'negative', '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": "['Be sure to try the oyster roll .']", "output": "[['oyster 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": "[\"I 'm astonished that this restaurant is categorized as $ $ $ rather than $ $ $ $ .\"]", "output": "[['restaurant', 'restaurant prices', 'negative', 'astonished']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 decent but nothing exceptional .']", "output": "[['NULL', 'food quality', 'neutral', 'nothing 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": "[\"Service was kind of slow , our waitress took forever to give us our check even though it was n't that busy .\"]", "output": "[['Service', 'service general', 'negative', 'slow'], ['waitress', 'service general', 'negative', 'took 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": "['\u2013 Great drinks , corn beef hash , coffee , B Fast burritos , Gluten Free menu .']", "output": "[['drinks', 'drinks quality', 'positive', 'Great'], ['corn beef hash', 'food quality', 'positive', 'Great'], ['coffee', 'drinks quality', 'positive', 'Great'], ['B Fast burritos', 'food quality', 'positive', 'Great'], ['menu', '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": "['The lunch menu is an awesome deal !']", "output": "[['lunch menu', 'food prices', '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": "['Took forever to get our order taken , water refills were too much to ask for and the only time she was fast was when we asked for our bill when we could get her attention .']", "output": "[['NULL', 'service general', 'negative', '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 waiter was a bit unfriendly and the feel of the restaurant was crowded .']", "output": "[['waiter', 'service general', 'negative', 'unfriendly'], ['feel', 'ambience general', '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": "['\u2013 I recently had the pleasure of dining as this delightful restaurant on 2nd street and wow what a great evening we had .']", "output": "[['restaurant', 'restaurant 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": "[\"The specialty here is decadent pancakes , but I 've been back now four times , and I 've been wowed every time .\"]", "output": "[['pancakes', 'food quality', 'positive', 'decadent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['\u2013 Pretty cheap for sit down Mexican AND downtown .']", "output": "[['NULL', 'restaurant prices', 'positive', 'cheap'], ['NULL', 'location general', 'positive', 'downtown']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['The appetizer of oysters , lobster , crab ( small size ) made a perfect entre for my wife .']", "output": "[['appetizer of oysters , lobster , crab ( small size )', '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 just found out that you can have the place to yourself on nights and weekends for a private party - ca n't wait to celebrate my next birthday there .\"]", "output": "[['place', 'restaurant miscellaneous', 'positive', \"ca n't wait to celebrate\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "[\"\u2013 As with most restaurants in Seattle , Mioposto 's service was bad and the food was overpriced .\"]", "output": "[['service', 'service general', 'negative', 'bad'], ['food', '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": "['You can easily hear him though so it was a pleasant sound and not overbearing .']", "output": "[['NULL', 'ambience general', 'positive', 'pleasant'], ['NULL', 'ambience general', 'positive', 'overbearing']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 there and see for yourself .']", "output": "[['NULL', '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": "[\"It 's a great little place with tons of potential to be a neighborhood joint if the service were n't so impersonal and corporate-like .\"]", "output": "[['place', 'restaurant general', 'positive', 'great little'], ['service', 'service general', 'negative', 'impersonal'], ['service', 'service general', 'negative', 'corporate-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": "[\"Brunch at Murphy 's is to die for , my specialty ... egg white omelet , the food is always freshly prepared .\"]", "output": "[['Brunch', 'food quality', 'positive', 'die for'], ['food', 'food quality', 'positive', 'freshly prepared'], ['egg white omelet', 'food quality', 'positive', 'specialty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Mama Mia \u2013 I live in the neighborhood and feel lucky to live by such a great pizza place .']", "output": "[['pizza 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": "['\u2013 tucked away over by the Beverly Center .']", "output": "[['NULL', 'location general', 'neutral', 'tucked 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 staff is pretty friendly .']", "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": "['And the service was simply spendid - quite a delight .']", "output": "[['service', 'service general', 'positive', 'spendid']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "[\"Ok ... \u2013 Maybe I went in on someone 's bad day ...\"]", "output": "[['NULL', 'restaurant 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 've had the Jellyfish , Horse Mackerel , Blue Fin Tuna and the Sake Ikura roll among others , and they were all good .\"]", "output": "[['Jellyfish', 'food quality', 'positive', 'good'], ['Horse Mackerel', 'food quality', 'positive', 'good'], ['Blue Fin Tuna', 'food quality', 'positive', 'good'], ['Sake Ikura roll', '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": "['WHAT ELSE CAN YOU SAY NICE PEOPLE AMAZING FOOD WOW']", "output": "[['FOOD', 'food quality', 'positive', 'AMAZING'], ['PEOPLE', '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": "['Always 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": "['My mouth felt very dry afterwards and I had that signature ? MSG ? taste lingering in my throat after I left the restaurant .']", "output": "[['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": "['Oh yeah ... the view was good , too .']", "output": "[['view', 'location 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 manager continually interrupted with `` Is there anything else I can do for you ? `` , a strange comment because she had hardly listened , let alone responded to our expression of disappointment at our experience .']", "output": "[['manager', '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": "['The service ranges from mediocre to offensive .']", "output": "[['service', 'service general', '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": "['Fresh ingrediants and super tasty .']", "output": "[['ingrediants', 'food quality', 'positive', 'Fresh'], ['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": "['the ambiance of the restaurant was nice and good for fine dinning .']", "output": "[['ambiance', '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": "['And the upstairs is a great place to hang out .']", "output": "[['upstairs', '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": "['\u2013 Not bad .']", "output": "[['NULL', 'restaurant general', 'neutral', 'Not 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 had the Kafta plate and it was perfect .']", "output": "[['Kafta plate', '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": "['\u2013 This place is famous for their breakfast .']", "output": "[['breakfast', 'food quality', 'positive', 'famous']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Waited 35 minutes for a table for 8 which was ok for such a big crowd .']", "output": "[['NULL', '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": "['The entire staff was extremely accomodating and tended to my every need .']", "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": "['There was a really nice vibe about the place ... good music , atmosphere and happy looking people .']", "output": "[['music', 'ambience general', 'positive', 'good'], ['atmosphere', 'ambience general', 'positive', 'good'], ['vibe', 'ambience general', 'positive', 'nice'], ['people', 'restaurant miscellaneous', '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": "[\"Try the Chef 's Choice for sushi as the smoked yellowtail was incredible and the rolls were also tasty .\"]", "output": "[['rolls', 'food quality', 'positive', 'tasty'], ['smoked yellowtail', 'food quality', 'positive', 'incredible'], [\"Chef 's Choice for sushi\", '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 sushi is as fresh as it comes ? you 'd think ocean was in their backyard , no joke !\"]", "output": "[['sushi', '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": "['food was fine , with a some little-tastier-than-normal salsa .']", "output": "[['food', 'food quality', 'positive', 'fine'], ['salsa', 'food quality', 'positive', 'little-tastier-than-normal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 finicky sushi eater and those who have sampled the best NYC has to offer , the fish is the freshest and the service is superb .']", "output": "[['fish', 'food quality', 'positive', 'freshest'], ['service', 'service general', '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": "['Old Reliable']", "output": "[['NULL', '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": "['One of the best Sushi place in town .']", "output": "[['Sushi 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": "[\"Food wise , its ok but a bit pricey for what you get considering the restaurant is n't a fancy place .\"]", "output": "[['Food', 'food quality', 'neutral', 'ok'], ['restaurant', 'restaurant prices', 'negative', 'pricey'], ['restaurant', 'ambience general', 'neutral', \"is n't a fancy\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['\u2013 Love their pizza , especially the mushroom pizza .']", "output": "[['pizza', 'food quality', 'positive', 'Love'], ['mushroom pizza', '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": "['They came in their own nifty take out box and with some homemade frosting ; very light and not-too-sweet .']", "output": "[['NULL', 'food quality', 'positive', 'very light'], ['NULL', 'food quality', 'positive', 'not-too-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": "['Enjoyed the food']", "output": "[['food', '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 've been to this restaurant over a dozen times with no complaints to date .\"]", "output": "[['restaurant', 'restaurant general', 'positive', 'no 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": "[\"Prefer to order it and pick it up though because I do n't like the servers , one young woman in particular .\"]", "output": "[['servers', 'service general', 'negative', \"do n't like\"], ['young woman', 'service general', 'negative', \"do n't 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": "['awesome find']", "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": "[\"It was n't the freshest seafood ever , but the taste and presentation was OK .\"]", "output": "[['seafood', 'food style_options', 'neutral', 'OK'], ['seafood', 'food quality', 'neutral', \"was n't the freshest\"], ['seafood', '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": "['However , the value and service are both severely lacking .']", "output": "[['service', 'service general', 'negative', 'lacking'], ['NULL', 'restaurant prices', '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": "['In Grammercy/Union Square/East Village this is my neighbors and my favorite spot .']", "output": "[['spot', '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 side of potatoes is to die for , as is the labne ( yogurt dip ) .']", "output": "[['side of potatoes', 'food quality', 'positive', 'die for'], ['labne ( yogurt dip )', 'food quality', 'positive', 'side of potatoes']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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', 'restaurant 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": "['What a tastly , flaky treat !']", "output": "[['NULL', 'food quality', 'positive', 'tastly'], ['NULL', 'food quality', 'positive', 'flaky']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 , better Margaritas !']", "output": "[['food', 'food quality', 'positive', 'Great'], ['Margaritas', 'drinks 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 service is really attentive and charming .']", "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": "['Most importantly , we were so excited about the food after seeing the very creative menu .']", "output": "[['menu', 'food style_options', 'positive', '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": "['A cozy spot for 2']", "output": "[['spot', '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": "['The best thing is , the prices are also quite 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": "[\"She promised to speak to the waitress who had flown off in a rage , but we could hardly take her promise seriously , seeing as she hadn 't bothered to get the waitresses name .\"]", "output": "[['NULL', 'service general', 'negative', 'flown off in a rage ']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['The chana masala ( garbanzo beans ) are also excellent .']", "output": "[['chana masala ( garbanzo beans )', '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 establishment really made a marked decline after ( and this is recurring story ) the airing of FOOD TELEVISIONS `` DINERS , DRIVE-INS , AND DIVES `` hosted by Guy Fieri , in which Schooner or Later was subject of .']", "output": "[['establishment', 'restaurant general', 'negative', 'decline']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 Dungeness crabs and at Ray 's you can get them served in about 6 different ways !\"]", "output": "[['Dungeness crabs', 'food 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": "['Food was good and appetizing .']", "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": "['It was absolutely amazing .']", "output": "[['NULL', 'restaurant 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": "['This is a great place to get a delicious meal .']", "output": "[['meal', '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": "[\"plus , i am allergic to rice , and the waitstaff was unbelievably accomodating -- did n't even bat an eye !\"]", "output": "[['waitstaff', '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": "['\u2013 I was highly disappointed in the food at Pagoda .']", "output": "[['food', '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": "['Overpriced']", "output": "[['NULL', 'restaurant 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": "['This is sad for what once was one of the best places you could ever eat .']", "output": "[['NULL', 'restaurant general', 'negative', 'sad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 're inside , the real experience begins .\"]", "output": "[['NULL', 'restaurant general', 'positive', 'real 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": "['and the waiter suggested a perfect sake ! !']", "output": "[['sake', 'drinks 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": "['The owner is belligerent to guests that have a complaint .']", "output": "[['owner', 'service general', 'negative', 'belligerent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 you want and more , very fresh .']", "output": "[['NULL', 'food quality', 'positive', 'fresh'], ['NULL', 'food style_options', 'positive', 'want and more']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 enjoyed the grilled Alaskan King Salmon with delectable creamed Washington russet potatoes and crisp green beans .']", "output": "[['grilled Alaskan King Salmon', 'food quality', 'positive', 'enjoyed'], ['creamed Washington russet potatoes', 'food quality', 'positive', 'delectable'], ['green beans', 'food quality', 'positive', 'crisp']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 , filet mignon was probably the best I 've ever try .\"]", "output": "[['food', 'food quality', 'positive', 'good'], ['filet mignon', '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": "['\u2013 Mercedes restaurant is so tasty , the service is undeniably awesome !']", "output": "[['service', 'service general', 'positive', 'awesome'], ['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": "['I can highly recommend their various saag and paneer and korma .']", "output": "[['saag', 'food quality', 'positive', 'recommend'], ['paneer', 'food quality', 'positive', 'recommend'], ['korma', '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 food is great and they make a mean bloody mary .']", "output": "[['food', 'food quality', 'positive', 'great'], ['bloody mary', 'drinks quality', 'positive', 'mean']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 food but no spice !']", "output": "[['food', 'food quality', 'positive', 'Nice'], ['food', 'food quality', 'negative', 'no spice']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Another plus is the open feel of the restaurant with glass walls on all sides .']", "output": "[['feel', 'ambience general', 'positive', 'plus'], ['feel', 'ambience general', 'positive', 'open']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Yum !']", "output": "[['NULL', 'food quality', 'positive', 'Yum']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['The bathroom itself is very small with two toilets and only one sink , the girl was staying totally on the way hanging out paper towels from the dispenser .']", "output": "[['bathroom', 'restaurant miscellaneous', '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": "['Love the enchiladas and chicken soup - and be sure to check out their specials .']", "output": "[['enchiladas', 'food quality', 'positive', 'Love'], ['chicken soup', 'food quality', 'positive', 'Love'], ['specials', 'food quality', 'positive', 'be sure to check 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": "['The food is simply unforgettable !']", "output": "[['food', 'food quality', 'positive', 'unforgettable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Management should really take notice and train their waitstaff and teach them some proper manners .']", "output": "[['waitstaff', 'service general', 'negative', 'teach them some proper manners']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 are friendly and the decor was ethic and colorful .']", "output": "[['staff', 'service general', 'positive', 'friendly'], ['decor', 'ambience general', 'positive', 'ethic'], ['decor', 'ambience general', 'positive', 'colorful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 Chuwam Mushi I have ever had .']", "output": "[['Chuwam Mushi', '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": "['Portions was just enough for me , but may not be for a big eater .']", "output": "[['Portions', 'food style_options', 'neutral', '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": "['Try the Pizza Ensalata !']", "output": "[['Pizza Ensalata', '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": "['Still I would recommend this place .']", "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": "['\u2013 The sushi here is perfectly good , but for $ 5 a piece , either the slices of fish should be larger , or there should be no pretense that this is a moderately priced restaurant ( even for NYC ) .']", "output": "[['sushi', 'food quality', 'positive', 'perfectly good'], ['restaurant', 'restaurant prices', 'negative', 'no pretense that this is a moderately priced restaurant'], ['sushi', 'food style_options', 'negative', 'should be larger'], ['sushi', 'food prices', 'negative', '$ 5 a piece']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 bound to have a very charming time .']", "output": "[['NULL', 'restaurant 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": "['We have since returned and also had a great experience , sampling more small plates and a variety of the beer ( cold and good ) .']", "output": "[['beer', 'drinks quality', 'positive', 'good'], ['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": "['Best Sushi in town .']", "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 staff was very nice and courteous and obviously chinese .']", "output": "[['staff', 'service general', 'positive', 'nice'], ['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": "['We ordered a selection of the small plates , and the shoe string onions , goat cheese pizza , grilled asparagus and fried brie with fruit were all very good .']", "output": "[['shoe string onions', 'food quality', 'positive', 'good'], ['goat cheese pizza', 'food quality', 'positive', 'good'], ['grilled asparagus', 'food quality', 'positive', 'good'], ['fried brie with fruit', '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": "['Nice job !']", "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": "['Though , one thing I realized later on was that the restaurant either used MSG or a meat tenderizer on the steak .']", "output": "[['steak', 'food quality', 'negative', 'used 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": "['Great selection of sakes .']", "output": "[['selection of sakes', 'drinks 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": "['Kudos to these guys .']", "output": "[['NULL', 'restaurant general', 'positive', 'Kudos']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "[\"Unless you are just stopping in for a few drinks I wouldn 't recommend going here .\"]", "output": "[['NULL', 'restaurant general', 'negative', \"wouldn 't 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 bottle of wine .']", "output": "[['bottle 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": "['The open sesame combo plate is a bargain for the heap of food given .']", "output": "[['open sesame combo plate', 'food prices', 'positive', 'bargain'], ['open sesame combo plate', 'food style_options', 'positive', 'heap']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 OVER-RATED ! ! ! !']", "output": "[['NULL', 'restaurant general', 'negative', 'OVER-RATED']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 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": "[\"Doesn 't get any better than that .\"]", "output": "[['NULL', 'restaurant general', 'positive', \"Doesn 't 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": "['best honey walnyt prawns that we have every tasted .']", "output": "[['honey walnyt prawns', '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": "['They have been featured on the food network and they deserve it .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'featured']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 the warm & cosy environment .']", "output": "[['environment', '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": "['Snotty Attitude']", "output": "[['NULL', 'service general', 'negative', 'Snotty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 aspiring , and the decor is festive and amazing ...']", "output": "[['atmosphere', 'ambience general', 'positive', 'aspiring'], ['decor', 'ambience general', 'positive', 'festive'], ['decor', 'ambience 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": "[\"Their specialty rolls are impressive , though I ca n't remember what we had .\"]", "output": "[['specialty rolls', 'food quality', '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": "['\u2013 I loved the pumpkin ravioli and the goat cheese gnocchi ( 5 big ones to a plate instead of 20 or so little gnocchis ) and my sister loved her filet mignon on top of spinach and mashed potatoes .']", "output": "[['pumpkin ravioli', 'food quality', 'positive', 'loved'], ['goat cheese gnocchi', 'food quality', 'positive', 'loved'], ['goat cheese gnocchi', 'food style_options', 'positive', 'loved'], ['filet mignon on top of spinach and mashed potatoes', '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": "['Not the biggest portions but adequate .']", "output": "[['portions', 'food style_options', 'neutral', 'Not the biggest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 with some friends , wait the half hour or so with a cup of joe , and enjoy more than your average breakfast .']", "output": "[['breakfast', 'food quality', 'positive', 'more than your average'], ['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": "['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": "['We were then charged for their most expensive sake ( $ 20 + per serving ) when we in fact drank a sake of less than half that price .']", "output": "[['NULL', 'service 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": "['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 chicken curry and chicken tikka masala are my favorite meat dishes .']", "output": "[['chicken curry', 'food quality', 'positive', 'favorite'], ['chicken tikka masala', '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": "['best restaurant in the world , great decor , great customer service , friendly manager']", "output": "[['restaurant', 'restaurant general', 'positive', 'best'], ['decor', 'ambience general', 'positive', 'great'], ['customer service', 'service general', 'positive', 'great'], ['manager', '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": "['Drinks were good .']", "output": "[['Drinks', '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 presentation of the food was an added bonus , it looked just as great as it tasted !']", "output": "[['food', 'food style_options', 'positive', 'added bonus'], ['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": "['Even then , the order was not correct and we were still waiting for a couple items .']", "output": "[['NULL', 'service general', 'negative', 'waiting']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['\u2013 We were treated very rudely here one time for breakfast .']", "output": "[['NULL', 'service general', 'negative', 'rudely']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 , spectacular location , and friendly service keep us coming back year after year .']", "output": "[['food', 'food quality', 'positive', 'Great'], ['location', 'location general', 'positive', 'spectacular'], ['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": "['WOW ! ! ! ! ! ! ! !']", "output": "[['NULL', 'restaurant general', 'positive', 'WOW']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['\u2013 Save yourself the waste of time & DO NOT visit .']", "output": "[['NULL', 'restaurant general', 'negative', 'DO NOT 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": "['Food was good and cheap .']", "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": "['Mmm ... 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": "[\"Its great if you spent the day there and did n't want to drive to eat .\"]", "output": "[['NULL', '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": "[\"All considered , I have to say that Ray 's Boathouse is deserving of its title as a Seattle institution .\"]", "output": "[[\"Ray 's Boathouse\", 'restaurant general', 'positive', 'deserving of its title']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 value sushi with high quality & nice setting .']", "output": "[['sushi', 'food prices', 'positive', 'Great value'], ['sushi', 'food quality', 'positive', 'high quality'], ['setting', '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": "['It was so much though that I could barely finish but I did , since it was delicious .']", "output": "[['NULL', 'food style_options', 'neutral', 'so much'], ['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 love this restaurant']", "output": "[['restaurant', '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": "['Not recommanded ! ! !']", "output": "[['NULL', 'restaurant general', 'negative', 'Not recommanded']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 stood there for 10 minutes while employees walked back and forth ignoring us .']", "output": "[['employees', 'service general', 'negative', 'ignoring']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 g/f and I both agreed the food was very mediocre especially considering the price .']", "output": "[['food', 'food quality', 'negative', 'mediocre'], ['food', 'food prices', '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": "['\u2013 The food is here is incredible , though the quality is inconsistent during lunch .']", "output": "[['food', 'food quality', 'positive', 'incredible'], ['lunch', '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": "['I have not a bad thing to say about this place .']", "output": "[['place', 'restaurant general', 'positive', 'have not a bad thing to say']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 you really have to warm up the pizza before it 's edible , even when you order ahead .\"]", "output": "[['pizza', 'food quality', 'negative', '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": "['\u2013 This is my `` must bring out of town guests to `` restaurant and they always enjoy and rave about it .']", "output": "[['restaurant', '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 had the kafta plate and I enjoyed it .']", "output": "[['kafta plate', '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": "['It was clear he didn \u2019 t really care .']", "output": "[['NULL', 'service general', 'negative', 'didn \\\\\u2019 t really 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 Caesar salad I ordered had so much lemon I could n't eat it .\"]", "output": "[['Caesar salad', 'food quality', 'negative', \"could n't 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": "[\"Our server continued to be attentive throughout the night , but I did remain puzzled by one issue : Who thinks that Ray 's is an appropriate place to take young children for dinner ?\"]", "output": "[['server', 'service general', 'positive', 'attentive'], [\"Ray 's\", 'restaurant miscellaneous', 'neutral', '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": "['Good Food , Great Service , Average Prices ( For the Strip )']", "output": "[['Food', 'food quality', 'positive', 'Good'], ['Service', 'service general', 'positive', 'Great'], ['NULL', 'restaurant prices', '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": "['6pm on a Sat before a Broadway play and we were quickly seated and served .']", "output": "[['NULL', 'service general', 'positive', 'quickly seated and served']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 pay such detail to everything from miso soup to complex rolls .']", "output": "[['miso soup', 'food quality', 'positive', 'pay such detail'], ['rolls', 'food quality', 'positive', 'complex']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 with tip .']", "output": "[['NULL', 'restaurant prices', 'positive', '$ 6']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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": "[\"In other words , if they aren 't making $ $ off of you then you do n't rate high on their 'service scale ' .\"]", "output": "[['service', 'service general', 'negative', \"do n't rate 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": "['\u2013 I really enjoyed my meal here .']", "output": "[['meal', '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": "['The music playing was very hip , 20-30 something pop music , but the subwoofer to the sound system was located under my seat , which became annoying midway through dinner .']", "output": "[['music', 'ambience general', 'positive', 'hip'], ['subwoofer to the sound system', '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": "['Holy Hummus !']", "output": "[['Hummus', 'food quality', 'positive', 'Holy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 my entr & eacute ; e , I completely enjoyed the seared Alaskan sea scallops complemented by chard , artichoke hearts , fennel , and pecorino toscano .']", "output": "[['seared Alaskan sea scallops', 'food quality', 'positive', 'enjoyed'], ['seared Alaskan sea scallops', 'food style_options', '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": "['The decor is rustic , traditional Japanese .']", "output": "[['decor', 'ambience general', 'neutral', 'rustic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 can the selection be innovative , but there 's a nice balance of traditional sushi as well .\"]", "output": "[['selection', 'food style_options', 'positive', 'innovative'], ['sushi', 'food style_options', 'positive', 'nice'], ['sushi', 'food style_options', '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": "['You are likely to be 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 great , the bartenders go that extra mile .']", "output": "[['food', 'food quality', 'positive', 'great'], ['bartenders', 'service general', 'positive', 'go that extra mile']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 offers an extensive wine list and an ambiance you wo n't forget !\"]", "output": "[['wine list', 'drinks style_options', 'positive', 'extensive'], ['ambiance', 'ambience general', 'positive', \"wo n't forget\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "[\"The naan was some of the best I 've had and I really enjoyed the bhartha , not too tomatoey .\"]", "output": "[['naan', 'food quality', 'positive', 'best'], ['bhartha', '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 the owners of Open Sesame ... Bravo ... I ca n't wait to come back to dine at your restaurant !\"]", "output": "[['Open Sesame', '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": "['the fish was fresh , though it was cut very thin .']", "output": "[['fish', 'food quality', 'positive', 'fresh'], ['fish', 'food style_options', 'negative', 'thin']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Been there lots since and its always 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": "['My favortie pizza joint in Seattle']", "output": "[['pizza joint', 'restaurant general', 'positive', 'favortie']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 place to all that want to try indain food for the first time .']", "output": "[['indain food', '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": "['Overrated']", "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": "['Worst Service I Ever Had']", "output": "[['Service', 'service general', 'positive', '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": "['As it turns out the owner was seated right next to us and when he came over to check on our problems was very dismissive and offered a token 20 % discount on our bill .']", "output": "[['owner', 'service general', 'negative', 'dismissive']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 with some friends one night to play bingo and watch the sox game and it was a blast !']", "output": "[['NULL', 'restaurant general', 'positive', 'blast']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['COMPLETELY OVER RATED !']", "output": "[['NULL', 'restaurant general', 'negative', 'OVER RATED']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['\u2013 Eggs , pancakes , potatoes , fresh fruit and yogurt -- everything they serve is delicious .']", "output": "[['Eggs', 'food quality', 'positive', 'delicious'], ['pancakes', 'food quality', 'positive', 'delicious'], ['potatoes', 'food quality', 'positive', 'delicious'], ['fresh fruit', 'food quality', 'positive', 'delicious'], ['yogurt', '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": "[\"\u2013 Ray 's is THE place to go for high quality seafood dinners .\"]", "output": "[['seafood dinners', 'food quality', 'positive', 'high 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 also really enjoy the simplicity of the decor and intimate feeling of a small restaurant .']", "output": "[['decor', 'ambience general', 'positive', 'enjoy'], ['decor', 'ambience general', 'positive', 'simplicity'], ['feeling', '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": "[\"We 're going back . : D\"]", "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": "['I should have thought to bring it up but never expected the food to be that mild .']", "output": "[['food', 'food quality', 'negative', 'mild']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 walked in , I was taken aback by their incredible wood decor .']", "output": "[['wood decor', '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": "[\"It 's a tiny place so if you get there before 8pm on a weekend ( Thurs ? Sun ) you will find it easier to get a table or a seat at the sushi bar .\"]", "output": "[['place', 'restaurant miscellaneous', 'neutral', '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": "['Best Neighborhood Standby .']", "output": "[['Standby', '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 'd go back there in a heartbeat .\"]", "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 's *very * reasonably priced , esp for the quality of the food .\"]", "output": "[['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": "['plenty of food , trust me .']", "output": "[['food', 'food style_options', 'positive', 'plenty 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 's the perfect spot for a romantic date for 2 or a secret rendezvous !\"]", "output": "[['spot', 'restaurant miscellaneous', 'positive', 'perfect'], ['spot', '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": "['Also have great margaritas !']", "output": "[['margaritas', '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": "[\"It 's unpretentious and underground .\"]", "output": "[['NULL', 'ambience general', 'positive', 'unpretentious'], ['NULL', 'ambience general', 'positive', 'underground']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 the next best thing to my Moms cooking .']", "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": "[\"The best place for a leisure Sunday breakfast amidst yachts , then take a stroll through the nearby Farmer 's Market .\"]", "output": "[['place', '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": "['If you can , come to this place by boat and make it a whole evening .']", "output": "[['place', 'restaurant miscellaneous', 'positive', 'make it a whole evening']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 desert we had the mango ginger creme brulee ... oh la la yummy ! ! !']", "output": "[['mango ginger creme brulee', '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": "[\"Its worth the wait , especially since they 'll give you a call when the table is ready .\"]", "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": "['They have a wide variety of fish and they even list which oceans they come from ; Atlantic or Pacific .']", "output": "[['fish', 'food style_options', 'positive', 'wide 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": "[\"Well I guess it 's hard to be seated when one is invisible to the staff .\"]", "output": "[['staff', 'service general', '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": "['Great open and friendly ambience .']", "output": "[['ambience', 'ambience general', 'positive', 'Great open']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 brief conversation with the manager at the end of the meal was the greatest disappointment -- to say we had been `` blown off `` would be an understatement .']", "output": "[['manager', '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": "[\"Ray 's is something of a Seattle institution , but given its gorgeous Sound views , I had suspected that the accolades were more due to the scenery than to the food and service .\"]", "output": "[['Sound views', 'location 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 swore never to return for a warm beer and mediocre meal .']", "output": "[['beer', 'drinks quality', 'negative', 'warm'], ['meal', '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": "[\"If it 's nice outside , request for a table in the balcony .\"]", "output": "[['balcony', '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": "['Great food with an awesome atmosphere !']", "output": "[['food', 'food quality', 'positive', 'Great'], ['atmosphere', 'ambience 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": "['I ended the meal with the unusual dessert of a port and chocolate tasting ... yummy !']", "output": "[['dessert of a port and chocolate tasting', '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 got the shellfish and shrimp appetizer and it was alright .']", "output": "[['shellfish and shrimp appetizer', 'food quality', '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": "['The pizza \u2019 s are thin crust and the menu offers very creative combinations and toppings .']", "output": "[['menu', 'food style_options', 'positive', 'creative'], ['pizza \u2019 s', 'food style_options', 'positive', 'thin 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": "[\"It 's a great place to enjoy food and meet friends .\"]", "output": "[['food', 'food quality', 'positive', 'enjoy'], ['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 service was excellent , the coffee was good even by starbucks standards and the food was outstanding .']", "output": "[['service', 'service general', 'positive', 'excellent'], ['coffee', 'drinks quality', 'positive', 'good'], ['food', '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": "['This is the place to relax and enjoy the finest quality food the industry can offer .']", "output": "[['place', 'ambience general', 'positive', 'relax'], ['food', 'food quality', 'positive', 'finest 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 would go back for the wine experience alone .']", "output": "[['wine', 'drinks quality', '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 best calamari in Seattle !']", "output": "[['calamari', '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 presentation of Snooze is excellent and it is one of those places that you feel more sophisticated just for being there ; but peel back the layers and you have an overpriced IHOP with a high brow menu .']", "output": "[['Snooze', 'ambience general', 'positive', 'excellent'], ['menu', 'food style_options', 'negative', 'high brow'], ['Snooze', 'restaurant 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 complained to the manager , but he was not even apologetic .']", "output": "[['manager', 'service general', 'negative', 'not even apologetic']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 more than just a great view !']", "output": "[['view', '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 atmosphere was just okay .']", "output": "[['atmosphere', 'ambience general', 'neutral', 'okay']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 seated it took about 30 minutes to finally get the meal .']", "output": "[['NULL', 'service general', 'negative', '30 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": "['\u2013 In a age of incremental cost cutting in restaurants , its nice to see a place that bucks that trend , and just plain delivers high quality food and good service , period .']", "output": "[['food', 'food quality', 'positive', 'high quality'], ['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 liked the atmosphere very much but the food was not worth the price .']", "output": "[['atmosphere', 'ambience general', 'positive', 'liked'], ['food', 'food quality', 'negative', 'not worth the price'], ['food', 'food prices', 'negative', 'not worth 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": "[\"It 's located in a strip mall near the Beverly Center , not the greatest location , but the food keeps me coming back for more .\"]", "output": "[['location', 'location general', 'neutral', 'not the greatest'], ['food', 'food quality', '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 my meal I had to send back my eggs for a simple request of breaking the yokes before cooking , and would have had to send them back again if I hadn 't rejected the meal all together .\"]", "output": "[['eggs', 'food quality', 'negative', 'send back'], ['meal', 'food quality', 'negative', 'rejected']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['The band was very good and the service was attentive .']", "output": "[['band', 'ambience general', 'positive', 'good'], ['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": "['\u2013 How to describe the best sushi in NYC : hmmmm , delicious , amazing , fantastic , suculent , perfect , nah , all of the above .']", "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": "[\"\u2013 Schooner or Later 's charming location along the marina in Long Beach and average food does not , unfortunately , compensate for its very poor customer service .\"]", "output": "[['location along the marina in Long Beach', 'location general', 'positive', 'charming'], ['food', 'food quality', 'neutral', 'average'], ['customer 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": "['We were seated right away , the table was private and nice .']", "output": "[['table', 'ambience general', 'positive', 'private'], ['table', '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": "[\"good sake , good food \u2013 i honestly do n't know much about japanese food at all .\"]", "output": "[['sake', 'drinks quality', 'positive', 'good'], ['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": "['Bring your cell phone cause you may have to wait to get into the best sushi restaurant in the world : BLUE RIBBON SUSHI .']", "output": "[['BLUE RIBBON SUSHI', '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": "['In the end our check came to $ 27 for 4 small pancakes , a breakfast burrito , an orange juice and an iced tea ( I had water ) .']", "output": "[['pancakes', 'food style_options', 'negative', 'small'], ['pancakes', 'food prices', 'negative', '$ 27'], ['breakfast burrito', 'food prices', 'negative', '$ 27'], ['orange juice', 'drinks prices', 'negative', '$ 27'], ['iced tea', 'drinks prices', 'negative', '$ 27']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 catering is out of this world , and Raouls chicken vegetable soup rocks my world ! ! !']", "output": "[['Raouls chicken vegetable soup', 'food quality', 'positive', 'rocks my world'], ['catering', '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": "['I should have just asked for the check when I saw that ; but their menu was so unique that I continued .']", "output": "[['menu', '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": "['Great Breakfast']", "output": "[['Breakfast', '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": "['Seabass on lobster risotto was the best .']", "output": "[['Seabass on lobster risotto', '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": "['\u2013 The atmosphere is great for any special occasion you might want to celebrate .']", "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": "['Great seasonal fish and seafood , with a classy waterfront setting .']", "output": "[['seasonal fish', 'food quality', 'positive', 'Great'], ['seafood', 'food quality', 'positive', 'Great'], ['waterfront setting', 'ambience general', 'positive', 'classy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 \u2019 s are not huge and the crust is thin ... keep that in mind when you \u2019 re ordering .']", "output": "[['pizza \u2019 s', 'food style_options', 'neutral', 'not huge'], ['crust', 'food style_options', 'neutral', 'thin']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 positive thing about Mioposto is the nice location .']", "output": "[['location', '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 appreciate their delivery too .']", "output": "[['delivery', 'service general', 'positive', 'appreciate']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 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 pizza is delicious and the salads are fantastic .']", "output": "[['pizza', 'food quality', 'positive', 'delicious'], ['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": "['Freshest sushi \u2013 I love this restaurant .']", "output": "[['sushi', 'food quality', 'positive', 'Freshest'], ['restaurant', '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 steak was done to my exact liking ( medium rare ) and was nice and juicy .']", "output": "[['steak', '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": "[\"Seriously , you ca n't go wrong , if it is unpretentious local fun you seek .\"]", "output": "[['NULL', 'ambience general', 'positive', \"ca n't go wrong\"], ['NULL', 'ambience general', 'positive', 'unpretentious local 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 owners are great fun and the beer selection is worth staying for .']", "output": "[['owners', 'service general', 'positive', 'great'], ['beer selection', 'drinks style_options', 'positive', 'worth staying 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": "['Great draft and bottle selection and the pizza rocks .']", "output": "[['pizza', 'food quality', 'positive', 'rocks'], ['draft and bottle selection', 'drinks 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": "[\"I guarantee you wo n't be disappointed , there 's also valet parking .\"]", "output": "[['NULL', 'restaurant general', 'positive', \"wo n't be disappointed\"], ['NULL', 'restaurant miscellaneous', 'positive', 'valet parking']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Late night dinning with exeptional food .']", "output": "[['food', 'food quality', 'positive', 'exeptional']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 big though , so do not order too much .']", "output": "[['portions', 'food style_options', 'neutral', '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": "[\"Definitely has one of the best jukebox 's i 've seen in a long long time .\"]", "output": "[[\"jukebox 's\", '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": "['Service was decent .']", "output": "[['Service', 'service general', '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": "['Absolutely 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": "['Oh , and the cheese fries are awesome !']", "output": "[['cheese fries', '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": "['Everyone that sat in the back outside agreed that it was the worst service we had ever received .']", "output": "[['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": "['I may not be a sushi guru but I can tell you that the food here is just okay and that there is not much else to it .']", "output": "[['food', 'food quality', 'negative', 'okay']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 , nice ambience , fairly expensive']", "output": "[['food', 'food quality', 'positive', 'Excellent'], ['ambience', 'ambience general', 'positive', 'nice'], ['NULL', 'restaurant 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": "['Melt in your mouth nigiri and sashmi , and very tasty rolls too .']", "output": "[['nigiri', 'food quality', 'positive', 'Melt in your mouth'], ['sashmi', 'food quality', 'positive', 'Melt in your mouth'], ['rolls', '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": "['Finally a meal that you will remember for a long time !']", "output": "[['meal', 'food quality', 'positive', 'remember for 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": "['Best food , phenominal service']", "output": "[['food', 'food quality', 'positive', 'Best'], ['service', 'service general', 'positive', 'phenominal']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 for a night when you want to splurge ! ( it was a bit expensive )']", "output": "[['NULL', 'restaurant general', 'positive', 'recommend'], ['NULL', 'restaurant 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 this diner had reasonably good food , the restaurant staff seemed completely indifferent to our presence , and this attitude was reflected in the lack of service .']", "output": "[['food', 'food quality', 'positive', 'good'], ['restaurant staff', 'service general', 'negative', 'indifferent']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 Comparison']", "output": "[['NULL', 'restaurant general', 'positive', 'No Comparison']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 about the prawns , they were fresh and had a slight crispiness about the batter ... soooo good ... the walnuts were cut in smaller pieces and very crunchy and tasty .']", "output": "[['prawns', 'food quality', 'positive', 'fresh'], ['batter', 'food quality', 'positive', 'crispiness'], ['walnuts', 'food quality', 'positive', 'crunchy'], ['walnuts', '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": "['I am so happy to have a wonderful Italian restaurant in my neighborhood .']", "output": "[['Italian 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": "['We waited for an hour to be seated .']", "output": "[['NULL', 'service general', 'negative', 'waited for 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": "[\"We put in our order and although we ordered 4 appetizers , the food did n't arrive until 45 minutes later ... WITH OUR MAIN COURSE .\"]", "output": "[['NULL', 'service general', 'negative', \"did n't arrive until 45 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": "['I can not wait to go back again this coming weekend !']", "output": "[['NULL', 'restaurant general', 'positive', 'can not wait to 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": "[\"If you 're interested in good tasting ( without the fish taste or smell ) , large portions and creative sushi dishes this is your place ...\"]", "output": "[['NULL', 'food quality', 'positive', 'good'], ['portions', 'food style_options', 'positive', 'large'], ['sushi dishes', 'food style_options', 'positive', '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": "['A perfect place to take out of town guests any time of the year .']", "output": "[['place', '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": "['I had a taste of all three items on her plate , and they were superb .']", "output": "[['plate', '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": "['Good for late night dining ( last minute planning ) without reservations .']", "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 am never disappointed with there food .']", "output": "[['food', 'food quality', 'positive', 'never 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": "['\u2013 By far the best bar in the east village ...']", "output": "[['bar', '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": "[\"It 's a great place to people watch .\"]", "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": "['Space was limited , but the food made up for it .']", "output": "[['Space', 'restaurant miscellaneous', 'negative', 'limited'], ['food', 'food quality', 'positive', 'made 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": "['The scallops are apparently cooked in a black olive butter which really makes them unique ( not to mention tasty ) .']", "output": "[['scallops', 'food quality', 'positive', 'tasty'], ['scallops', '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": "['This place is charming and relaxing .']", "output": "[['place', '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": "['not chewy at all .']", "output": "[['NULL', 'food quality', 'positive', 'not chewy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 only in Seattle for one night and I 'm so glad we picked Rays for dinner !\"]", "output": "[['Rays', '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": "['\u2013 I will never forget the amazing meal , service , and ambiance I experience at this restaurant .']", "output": "[['meal', 'food quality', 'positive', 'amazing'], ['service', 'service general', 'positive', 'amazing'], ['ambiance', 'ambience 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": "['Amazing Spanish Mackeral special appetizer and perfect box sushi ( that eel with avodcao -- um um um ) .']", "output": "[['Spanish Mackeral special appetizer', 'food quality', 'positive', 'Amazing'], ['box sushi', 'food quality', 'positive', 'perfect'], ['eel with avodcao', 'food quality', 'positive', 'um um um']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 weekends , you might have to wait for couple hours .']", "output": "[['NULL', 'service general', 'neutral', '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": "['\u2013 The food was not great & the waiters were rude .']", "output": "[['food', 'food quality', 'negative', 'not great'], ['waiters', '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 9 oz steak came next and it tasted great , at least initially .']", "output": "[['9 oz steak', '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 got there I sat up stairs where the atmosphere was cozy & the service was horrible !']", "output": "[['atmosphere', 'ambience general', 'positive', 'cozy'], ['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": "['The wine list is incredible and extensive and diverse , the food is all incredible and the staff was all very nice , good at their jobs and cultured .']", "output": "[['wine list', 'drinks style_options', 'positive', 'incredible'], ['wine list', 'drinks style_options', 'positive', 'extensive'], ['wine list', 'drinks style_options', 'positive', 'diverse'], ['food', 'food quality', 'positive', 'incredible'], ['staff', '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": "['\u2013 IT CANT GET ANY BETTER ! ! ! ! !']", "output": "[['NULL', 'restaurant general', 'positive', 'CANT 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": "['Sunday afternoons there is a band playing and it is lots of fun .']", "output": "[['band', '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": "['Everything , and I mean everything on the menu is delectable .']", "output": "[['menu', 'food quality', 'positive', 'delectable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 many people have their favorite types of pizza and pizza places , but Mioposto 's pizza lacks quality and good taste .\"]", "output": "[['pizza', 'food quality', 'negative', 'lacks quality and 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": "['There is only one place on the east coast that has it all , plus a lot more .']", "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": "['Always a winner .']", "output": "[['NULL', 'restaurant general', '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": "['The prior reviews said Kid friendly ... give me a break with two young children that is light years .']", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'light years']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 \u2019 s are light and scrumptious .']", "output": "[['pizza \u2019 s', 'food quality', 'positive', 'scrumptious'], ['pizza \u2019 s', 'food quality', 'positive', 'light']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 art on the walls , very colorful !']", "output": "[['art on the walls', 'ambience general', 'positive', 'colorful']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Mediocre food']", "output": "[['food', 'food quality', '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": "['I will never return again .']", "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": "[\"We had a very hard time getting the waitress ' attention and finally had to get up and go inside to speak to a manager .\"]", "output": "[['waitress', 'service general', 'negative', 'had a very hard 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": "['Never too crowded and always great service .']", "output": "[['service', 'service general', 'positive', 'great'], ['NULL', 'restaurant miscellaneous', 'positive', 'Never too 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": "['you are with a hot date and he /she has an urge for sushi ... then this might be the place .']", "output": "[['sushi', 'food quality', 'positive', 'this might be the 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": "[\"\u2013 I ca n't believe Murphy 's has been around for over 25 years , amazing .\"]", "output": "[[\"Murphy 's\", 'restaurant miscellaneous', '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": "['Can get busy on Fridays for a table but once seated , the service is so efficient you can be in and out of there quickly .']", "output": "[['service', 'service general', 'positive', 'efficient']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 served with either a peppercorn sauce or red wine reduction , though both were indistinguishable in taste .']", "output": "[['peppercorn sauce', 'food quality', 'neutral', 'indistinguishable'], ['red wine reduction', 'food quality', 'neutral', 'indistinguishable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['The nicest waiters in town .']", "output": "[['waiters', '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": "['The service was courteous and attentive .']", "output": "[['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": "[\"As of writing this I just tried their give away a link again & it still doesn 't work .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'negative', \"doesn 't work\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['any further needs we may have had could not have been met since no one stopped by the table .']", "output": "[['NULL', 'service general', 'negative', 'no one stopped by the table']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 Sushi , High Price']", "output": "[['Sushi', 'food quality', 'positive', 'Good'], ['Sushi', 'food 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": "['But the servers were extremely attentive and very friendly .']", "output": "[['servers', '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 have been to this place , folks and it is BAD .']", "output": "[['place', 'restaurant 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": "['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": "['It was like dining at a completely different restaurant .']", "output": "[['NULL', '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": "['Two thumbs up !']", "output": "[['NULL', 'food quality', '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": "[\"Imagine my happy surprise upon finding that the views are only the third-best thing about Ray 's !\"]", "output": "[['views', 'location general', 'positive', 'third-best thing'], [\"Ray 's\", 'restaurant general', 'positive', 'happy 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": "['Hands down , the best tuna I have ever had .']", "output": "[['tuna', '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": "[\"If you 're going to see Danny Gans or just staying at the Mirage , do n't miss this one .\"]", "output": "[['NULL', 'restaurant general', 'positive', \"do n't 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": "['great lunch spot']", "output": "[['lunch spot', '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 love breakfast here .']", "output": "[['breakfast', '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 sashimi was the freshest and most tender I have ever tasted .']", "output": "[['sashimi', 'food quality', 'positive', 'freshest']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 won \u2019 t be disappointed by their menu .']", "output": "[['menu', 'food style_options', 'positive', 'won \u2019 t be 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": "[\"Many people talk about the great pizza and poor service , so it ca n't just be the rantings of a few dissatisfied customers .\"]", "output": "[['pizza', 'food quality', 'positive', 'great'], ['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": "['I will 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": "['my service was stellar !']", "output": "[['service', 'service general', 'positive', 'stellar']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 for sure coming back to this restaurant .']", "output": "[['restaurant', '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": "['Will definitely 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": "[\"Maybe it is good for that one night once in a blue moon when the chefs decide to use fish that 's half-way decent .\"]", "output": "[['fish', 'food quality', 'negative', '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": "[\"\u2013 I 've been to Open Sesame only once , but I 'm still reeling from the experience ! !\"]", "output": "[['Open Sesame', 'restaurant general', 'positive', 'reeling']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['The pancakes were certainly inventive but $ 8.50 for 3 - 6 `` pancakes ( one of them was more like 5 `` ) in the pancake flight ( sample of 3 different pancakes ) is well over-priced .']", "output": "[['pancakes', 'food style_options', 'positive', 'inventive'], ['pancakes', 'food 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": "['HOWEVER , one Sunday afternoon my husband and I did go ( although with my loud protests ) and were 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": "['Well 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": "['Caution - its real food for people who love the best .']", "output": "[['food', 'food quality', '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": "['I have a but here - there was a bathroom attendant in the restroom which was odd .']", "output": "[['restroom', 'restaurant miscellaneous', 'negative', 'odd']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 dish are the honwy walnut prawns -- just outstanding .']", "output": "[['honwy walnut prawns', '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 pancakes should be larger ( at least 8 `` ) to justify the expense even with the unique offerings .']", "output": "[['pancakes', 'food style_options', 'negative', 'should be larger']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['\u2013 My first time to dine at this restaurant was with my son and it was absolutely horrible !']", "output": "[['restaurant', '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": "['Excellent']", "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": "['Parking is reasonably good , they have their own lot and you can park in the park nearby .']", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'reasonably 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": "['Caesar salad was superb .']", "output": "[['Caesar salad', '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": "[\"\u2013 I do n't understand how I was a stranger to this place for so long ... the fajita salad , the colorado , the fajitas - EVERYTHING is delicious .\"]", "output": "[['fajita salad', 'food quality', 'positive', 'delicious'], ['colorado', 'food quality', 'positive', 'delicious'], ['fajitas', '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": "['best chinese food i have tasted in a long time']", "output": "[['chinese 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 price was right too .']", "output": "[['NULL', 'restaurant prices', '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": "['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": "[\"We put out name down and although there were open tables we were told that it 'd be a 30 minute wait .\"]", "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": "['The coffe is very good , too .']", "output": "[['coffe', '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": "['We waited over 30 minutes for our drinks and over 1 1 /2 hours for our food .']", "output": "[['NULL', 'service general', 'negative', 'waited']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 behind the counter are always friendly and helpful .']", "output": "[['servers behind the counter', '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": "['Our waiter was non-existent and after our food finally arrived over an hour after we ordered , we were not given any water or utensils .']", "output": "[['waiter', '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": "['\u2013 This place is unbelievably over-rated .']", "output": "[['place', 'restaurant general', 'negative', 'over-rated']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 poor customer service .']", "output": "[['customer 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": "['We \u2019 re can \u2019 t say enough about their delicious gourmet pizza \u2019 s !']", "output": "[['pizza \u2019 s', '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": "['Furthermore , while the fish is unquestionably fresh , rolls tend to be inexplicably bland .']", "output": "[['fish', 'food quality', 'positive', 'fresh'], ['rolls', '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 only drawback is the crowded seating and the slow service .']", "output": "[['seating', 'restaurant miscellaneous', 'negative', 'crowded'], ['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": "['Not alot of smoking places left in New York , but I have found my favorite smoking balconey in the city .']", "output": "[['balconey', 'restaurant miscellaneous', '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": "['We shared the family platter and I especially enjoyed the black cod in sake kasu .']", "output": "[['black cod in sake kasu', '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": "['But the space is small and lovely , and the service is helpful .']", "output": "[['space', 'ambience general', 'positive', 'small'], ['space', 'ambience general', 'positive', 'lovely'], ['service', '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": "['It was 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": "['yummy .']", "output": "[['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": "['Delectable']", "output": "[['NULL', 'food quality', 'positive', 'Delectable']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['- Mediocre Service / Quality']", "output": "[['Service', 'service general', '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": "['\u2013 This is one of my top lunch spots , huge portions , fast service and amazing margaritas ! !']", "output": "[['NULL', 'restaurant general', 'positive', 'top lunch spots'], ['portions', 'food style_options', 'positive', 'huge'], ['service', 'service general', 'positive', 'fast'], ['margaritas', 'drinks 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": "['Best Crab Cakes in Town']", "output": "[['Crab Cakes', '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": "[\"Everything I 've had here is good , taco salads , burritos , enchiladas i love this place .\"]", "output": "[['taco salads', 'food quality', 'positive', 'good'], ['burritos', 'food quality', 'positive', 'good'], ['enchiladas', 'food quality', 'positive', 'good'], ['place', 'restaurant general', 'positive', 'love'], ['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": "['Great Pizza , Poor Service']", "output": "[['Pizza', 'food quality', 'positive', 'Great'], ['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": "['splendid']", "output": "[['NULL', 'restaurant general', 'positive', 'splendid']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 best friend had the chicken shawarma and she STILL raves about it being the best anywhere !']", "output": "[['chicken shawarma', '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": "['Chintzy portions']", "output": "[['portions', 'food style_options', 'negative', 'Chintzy']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 eats .']", "output": "[['eats', '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": "[\"Normally , places ask how hot you want it , but they did n't .\"]", "output": "[['NULL', 'service general', 'negative', \"but they did n't\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 always found the wait staff and , if you sit at the bar , the cooks very friendly .\"]", "output": "[['cooks', '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": "['Nothing on the menu is less than amazing .']", "output": "[['menu', '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": "[\"My party of two was feeling particularly ambitious , and we splurged on the Shilshole Sampler ... a beautiful assortment of enormous white gulf prawns , smoked albacore tuna , Ray 's fantastic manila clams seasoned with dill , scallops in a tasty soy dressing , and a tiny pile of Dungeness crab atop a sublime butter sauce .\"]", "output": "[['white gulf prawns', 'food style_options', 'positive', 'enormous'], ['manila clams', 'food quality', 'positive', 'fantastic'], ['soy dressing', 'food quality', 'positive', 'tasty'], ['butter sauce', 'food quality', 'positive', 'sublime'], ['Shilshole Sampler', 'food style_options', 'positive', 'a beautiful assortment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 frankly shocked when I read the bad reviews - this place is fantastic ; it has not let us down in any way , and we 've eaten here more than 10 times .\"]", "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": "['Your a sushi fan , you love expertly cut fish , great sake , a killer SOHO location , and of course : Salmon , Tuna , Fluke , Yellow Tail , Cod , Mackeral , Jellyfish , Sea Urchin , Shrimp , Lobster , Sea Bream , Trout , Milk Fish , Blue Fin Tuna , Eel , Crab , Sardine , Monk Fish , Roe , Scallop , Oysters , and a varity of Toro .']", "output": "[['sushi', 'food quality', 'positive', 'fan'], ['fish', 'food style_options', 'positive', 'love expertly cut'], ['sake', 'drinks quality', 'positive', 'great'], ['SOHO location', 'location general', 'positive', 'killer'], ['Toro', 'food style_options', 'positive', 'a varity 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": "['The service is fantastic at this fun place .']", "output": "[['service', 'service general', 'positive', 'fantastic'], ['place', '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": "['Also , there was only one bathroom stall- probably need more for such big crowds .']", "output": "[['bathroom stall', 'restaurant miscellaneous', 'negative', 'need more']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 without ever getting service .']", "output": "[['service', 'service general', 'negative', 'without ever']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['I liked it .']", "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": "['\u2013 I highly recommend Mioposto .']", "output": "[['Mioposto', '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 food is fantastic , authentic , delicious and very , very affordable .']", "output": "[['food', 'food quality', 'positive', 'fantastic'], ['food', 'food quality', 'positive', 'authentic'], ['food', 'food quality', 'positive', 'delicious'], ['food', 'food 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": "['I have eaten here three times and have found the quality and variety of the fish to be excellent .']", "output": "[['fish', 'food quality', 'positive', 'excellent'], ['fish', '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": "['But she is very friendly with certain people , making it even more annoying .']", "output": "[['NULL', 'service 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": "['The regular menu here is slightly above average that is not worth the snotty attitude that you receive .']", "output": "[['regular menu', 'food quality', 'neutral', 'above average'], ['regular menu', 'food quality', 'neutral', 'not worth the snotty attitude'], ['NULL', 'service general', 'negative', 'snotty']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 serve THE best hummus in America , with a drizzle of fragrant olive oil ( which , I believe is the traditional way ) !']", "output": "[['hummus', 'food quality', 'positive', 'best'], ['hummus', '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": "['great beer']", "output": "[['beer', '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": "['Seafood Plus']", "output": "[['Seafood', '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": "['it is sad there is not many people who frequent eating at places like these that look pricey because they are at the hotel ... but they are definitely one you dont want to miss , esp if your in downtown san jose .']", "output": "[['NULL', 'restaurant general', 'positive', 'you dont want to 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": "['They serve it in a tall , skinny hour-glass shaped glass to disguise the fact that you a getting a small juice at the price of a half gallon in a supermarket .']", "output": "[['juice', 'drinks style_options', 'negative', 'small'], ['juice', 'drinks prices', '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": "['The food was ok , but the service was so poor that the food was cold buy the time everyone in my party was served .']", "output": "[['food', 'food quality', 'neutral', 'ok'], ['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": "['great meal \u2013 the fish on the omikase platter was absolutely decadent -- there was none of the stringiness that sometimes accompanies fair sushi -- this fish was perfect ! ! ! !']", "output": "[['meal', 'food quality', 'positive', 'great'], ['fish on the omikase platter', 'food quality', 'positive', 'decadent'], ['fish on the omikase platter', '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": "['Drinks are suberb , and I feel like I am in a Third World country when I walk in the door .']", "output": "[['Drinks', 'drinks quality', 'positive', 'suberb'], ['NULL', 'ambience general', 'positive', 'in a Third World country']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 & Cool Place with the Best Pizza and Coffee']", "output": "[['Place', 'ambience general', 'positive', 'Cool'], ['Pizza', 'food quality', 'positive', 'Best'], ['Coffee', 'drinks 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": "['Green Tea creme brulee is a must !']", "output": "[['Green Tea creme brulee', '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": "['I found a new home and just moved with a long lease .']", "output": "[['NULL', 'restaurant general', 'positive', 'new 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": "[\"They aren 't the most talkative , but everytime I 've been there they have been very busy , which probably accounts for the lack of conversation .\"]", "output": "[['NULL', 'service general', 'neutral', \"aren 't the most talkative\"]]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 itself is not exactly the best I 've had EVER , but still pretty good .\"]", "output": "[['pizza', 'food quality', 'positive', 'not exactly the 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 did have the same waiter the second time , so maybe the service is spotty and our luck is good .']", "output": "[['waiter', '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": "[\"\u2013 Thats a big statement considering I 've been pulling crab traps and making the cakes myself since I was about seven - but something about these little devils gets better every time .\"]", "output": "[['cakes', '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": "['Fabulous Italian Food !']", "output": "[['Italian 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": "[\"However , this place is a gem , and I wo n't stop going back .\"]", "output": "[['place', 'restaurant general', 'positive', 'gem']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 gets really busy , so get there on the early side so you can grab a seat , if you do have to wait , its not bad because the service is 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": "['The dinner only took us about an hour and the prices were alright for the strip , almost $ 50 /pp after tax and tip ( about average ) .']", "output": "[['NULL', 'restaurant prices', '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": "['The onion rings are great !']", "output": "[['onion rings', '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 're in the area you shouldn 't be disappointed .\"]", "output": "[['NULL', 'restaurant general', 'positive', \"shouldn 't be 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": "['\u2013 Great financial district mexican spot .']", "output": "[['mexican spot', '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 house special roll is really good .']", "output": "[['house special roll', '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 waiters are very experienced and helpful with pairing your drink choice to your food tastes or vice versa .']", "output": "[['waiters', 'service general', 'positive', 'experienced']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Perfection .']", "output": "[['NULL', 'restaurant general', 'positive', 'Perfection']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['Poor customer service / poor pizza .']", "output": "[['customer service', 'service general', 'negative', 'Poor'], ['pizza', 'food quality', '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": "['Overall , I would go back and eat at the restaurant again .']", "output": "[['restaurant', '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": "['Server made several sake suggestions which were very good .']", "output": "[['sake', 'drinks quality', 'positive', 'good'], ['Server', 'service general', 'positive', 'suggestions']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 only negative comment is that I wish the pieces were a little bigger .']", "output": "[['pieces', 'food style_options', 'negative', 'negative']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 asparagus , which turned out to be incredible and perfectly prepared .']", "output": "[['asparagus', '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": "['What may be interesting to most is the worst sevice /attitude comes from the owners of this establishment .']", "output": "[['owners', '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 only drawback is that this place is really expensive and the portions are on the small side .']", "output": "[['place', 'restaurant prices', 'negative', 'expensive'], ['portions', '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": "['Terrible service , food ok , pricey']", "output": "[['service', 'service general', 'negative', 'Terrible'], ['food', 'food quality', 'neutral', 'ok'], ['NULL', '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": "['Although the service could be improved considering the money you put in .']", "output": "[['service', 'service general', 'negative', 'could be improved']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 routinely see Indian families and friends dining there , which is always a good sign .']", "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": "[\"If I want to stand in line on Sunday for an hour to get average brunch food , then I would put Murphy 's at the top of the list .\"]", "output": "[['brunch food', 'food quality', 'neutral', 'average'], ['NULL', 'service general', 'negative', '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": "['For the amount of food we got the prices should have been lower .']", "output": "[['food', 'food prices', 'negative', 'should have been lower']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 they are good at seating you promptly and have quick service .']", "output": "[['service', 'service general', 'positive', 'quick'], ['NULL', 'service general', 'positive', ' good at']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 wonderful and the food reminds me of my recent trip to Italy .']", "output": "[['wine list', 'drinks style_options', '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 was great , the margaritas too but the waitress was too busy being nice to her other larger party than to take better care of my friend and me .']", "output": "[['food', 'food quality', 'positive', 'great'], ['margaritas', 'drinks quality', 'positive', 'great'], ['waitress', 'service general', 'negative', 'too 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": "[\"I ca n't saybenough good things about this restaurant , and I ca n't wait for my next several visits .\"]", "output": "[['restaurant', '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": "['Love it every time']", "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": "['We are locals , and get the feeling the only way this place survives with such average food is because most customers are probably one-time customer tourists .']", "output": "[['food', 'food quality', 'negative', '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": "['Also love their caeser salad .']", "output": "[['caeser salad', '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": "[\"She doesn 't make you feel welcome and treats you like an annoyance .\"]", "output": "[['NULL', 'service general', 'negative', 'annoyance']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really 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 the right size for the menu .\"]", "output": "[['NULL', 'restaurant miscellaneous', 'positive', 'the right size']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 fantastic , and the waiting staff has been perfect every single time we 've been there .\"]", "output": "[['food', 'food quality', 'positive', 'fantastic'], ['waiting staff', '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 desert was the perfect ending to an almost perfect dinner .']", "output": "[['desert', 'food quality', 'positive', 'perfect'], ['dinner', '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 have been here , spent tons of money on a chef special dinner and it was a major dissappointment .']", "output": "[['chef special dinner', 'food quality', 'negative', 'dissappointment'], ['chef special dinner', 'food prices', 'negative', 'dissappointment']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 A NEW HOME ON SECOND AVENUE ! ! ! ! ! ! ! ! ! ! ! !']", "output": "[['NULL', 'restaurant general', 'positive', 'NEW 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": "['\u2013 Had to wait thirty minutes to get in the door on a tuesday morning , but it was so worth it .']", "output": "[['NULL', 'restaurant miscellaneous', 'negative', 'wait thirty minutes'], ['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": "['\u2013 It is sad to see a place that was once `` THE `` place to meet and eat for Bfast or Lunch , now be the place that is a big `` DONT BOTHER . ``']", "output": "[['place', 'restaurant general', 'negative', 'sad']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]' "} {"task_type": "generation", "dataset": "absa-quad", "input": "['The lemon chicken tasted like sticky sweet donuts and the honey walnut prawns , the few they actually give you ... were not good .']", "output": "[['lemon chicken', 'food quality', 'negative', 'sticky sweet'], ['honey walnut prawns', 'food quality', 'negative', 'not good'], ['honey walnut prawns', 'food style_options', '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": "['It was romantic - and even nice even with my sister , reminded me of Italy , and had artwork and music that kept up the feeling of being in a Mediterrean villa .']", "output": "[['NULL', 'ambience general', 'positive', 'romantic'], ['artwork', 'ambience general', 'positive', 'kept up the feeling of being in a Mediterrean villa'], ['music', 'ambience general', 'positive', 'kept up the feeling of being in a Mediterrean villa']]", "situation": "none", "label": "", "extra": "", "instruction": "Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. Example: Sentence: \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty\" 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 excellent and the wait staff was quick .']", "output": "[['sushi', 'food quality', 'positive', 'excellent'], ['wait staff', '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": "['In fact , many want to return a second time during their visit .']", "output": "[['NULL', 'restaurant general', 'positive', 'return a second 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": "['\u2013 Best Mexican place for lunch in the financial district .']", "output": "[['Mexican 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 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": "['the atmosphere is 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": "['Perfect on a cold day .']", "output": "[['NULL', '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']]' "}