| > 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。 | |
| #### 以acos数据集中抽取的jsonl文件一条数据举例： | |
| ``` | |
| { | |
| "task_type": "generation", | |
| "dataset": "acos", | |
| "input": ["the computer has difficulty switching between tablet and computer ."], | |
| "output": "[['computer', 'laptop usability', 'negative', 'difficulty']]", | |
| "situation": "none", | |
| "label": "", | |
| "extra": "", | |
| "instruction": " | |
| Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. | |
| Input: A sentence | |
| Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence. | |
| Example: | |
| Sentence: \"Also it's not a true SSD drive in there but eMMC, which makes a difference.\" | |
| Output: [['SSD drive', 'hard_disc operation_performance', 'negative', 'NULL']]' | |
| " | |
| } | |
| ``` | |
| > 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。 | |
| #### 原始数据集 | |
| - 数据[链接](https://github.com/zhijing-jin/ARTS_TestSet) | |
| - Paper: [Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis](https://arxiv.org/pdf/2009.07964.pdf) | |
| - 说明：原始数据集由laptop和restaurant两个领域的的json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，该数据的提出目的是测试模型鲁棒性，因此在引用该数据集的文章中多是通过在一个领域的数据上训练，在该数据集的另一个领域上测试。 | |
| #### 当前SOTA | |
| *数据来自[论文](https://arxiv.org/abs/2303.02846)* | |
| - 评价指标：macro-averaged F1 | |
| - SOTA模型：CVIB | |
| - 其他领域数据训练后在restaurant数据集上macro-averaged F1：**70.29** | |
| - restaurant数据集上训练并测评的macro-averaged F1：**82.03** | |
| - 其他领域训练后在laptop上测评的macro-averaged F1：**69.39** | |
| - laptop数据集上训练并测评的macro-averaged F1：**77.53** ) | |
| - Paper：[Reducing Spurious Correlations for Aspect-Based Sentiment Analysis with Variational Information Bottleneck and Contrastive Learning](https://arxiv.org/pdf/2303.02846.pdf) | |
| - 说明：该论文来自[Google Scholar](https://scholar.google.com/scholar?as_ylo=2023&q=ABSA+ARTS&hl=zh-CN&as_sdt=0,5)检索到的引用ARTS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。 | |