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--- |
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task_categories: |
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- text-generation |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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--- |
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> 上述数据集为ABSA(Aspect-Based Sentiment Analysis)领域数据集，基本形式为从句子中抽取：方面术语、方面类别(术语类别)、术语在上下文中情感极性以及针对该术语的观点词，不同数据集抽取不同的信息，这点在jsonl文件的“instruction”键中有分别提到，在此我将其改造为了生成任务，需要模型按照一定格式生成抽取结果。 |
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#### 以acos数据集中抽取的jsonl文件一条数据举例： |
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``` |
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{ |
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"task_type": "generation", |
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"dataset": "acos", |
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"input": ["the computer has difficulty switching between tablet and computer ."], |
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"output": "[['computer', 'laptop usability', 'negative', 'difficulty']]", |
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"situation": "none", |
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"label": "", |
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"extra": "", |
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"instruction": " |
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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. |
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Input: A sentence |
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Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. |
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Example: |
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Sentence: \"Also it's not a true SSD drive in there but eMMC, which makes a difference.\" |
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Output: [['SSD drive', 'hard_disc operation_performance', 'negative', 'NULL']]' |
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" |
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} |
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``` |
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> 此处未设置label和extra，在instruction中以如上所示的字符串模板，并给出一个例子进行one-shot，ABSA领域数据集(absa-quad,acos,arts,aste-data-v2,mams,semeval-2014,semeval-2015,semeval-2016,towe)每个数据集对应instruction模板相同，内容有细微不同，且部分数据集存在同一数据集不同数据instruction内容不同的情况。 |
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#### 原始数据集 |
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- 数据[链接](https://github.com/IsakZhang/ABSA-QUAD) |
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- Paper: [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://aclanthology.org/2021.emnlp-main.726.pdf) |
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- 说明：原始数据集由Rest15和Rest16两个文件夹的数据组成，本次改造我将两个数据集的数据合并并区分为train、validation与test |
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#### 当前SOTA |
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*数据来自[论文](https://arxiv.org/abs/2305.09193)* |
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- 评价指标：F1 score |
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- SOTA模型：E2H-large (Rest15上F1 Score：**52.39** ， Rest16上F1 Score：**61.86**) |
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- Paper：[Easy-to-Hard Learning for Information Extraction](https://arxiv.org/pdf/2305.09193.pdf) |
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- 说明：该论文来自[Google Scholar](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=2005&sciodt=0,5&cites=13359676136585163616&scipsc=&q=&scisbd=1)检索到的引用ABSA-QUAD原论文的论文之一，我比较了2023年的一些论文工作后筛选了一个最优指标以及模型。 |
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