论文标题
美味的汉堡，潮湿的薯条：基于方面的情感分析中的方面鲁棒性
Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis
论文作者
论文摘要
基于方面的情感分析（ABSA）旨在预测文本中特定方面的情绪。但是，现有的ABSA测试集不能用于探测模型是否可以将目标方面的情感与非目标方面区分开。为了解决这个问题，我们开发了一种简单但有效的方法来丰富ABSA测试集。具体而言，我们生成了新的示例，以消除目标方面情感的非目标方面的混杂情感。基于Semeval 2014数据集，我们构建了鲁棒性测试集（ARTS）作为ABSA模型鲁棒性的全面探索。通过人类评估，超过92％的艺术数据表现出高流利性和所需的情感。使用艺术，我们分析了九种ABSA模型的鲁棒性，令人惊讶地观察到它们的准确性下降了高达69.73％。我们探索了改善方面鲁棒性的几种方法，并发现对抗性训练可以将模型的艺术表现提高到32.85％。我们的代码和新测试集可在https://github.com/zhijing-jin/arts_testset上找到
Aspect-based sentiment analysis (ABSA) aims to predict the sentiment towards a specific aspect in the text. However, existing ABSA test sets cannot be used to probe whether a model can distinguish the sentiment of the target aspect from the non-target aspects. To solve this problem, we develop a simple but effective approach to enrich ABSA test sets. Specifically, we generate new examples to disentangle the confounding sentiments of the non-target aspects from the target aspect's sentiment. Based on the SemEval 2014 dataset, we construct the Aspect Robustness Test Set (ARTS) as a comprehensive probe of the aspect robustness of ABSA models. Over 92% data of ARTS show high fluency and desired sentiment on all aspects by human evaluation. Using ARTS, we analyze the robustness of nine ABSA models, and observe, surprisingly, that their accuracy drops by up to 69.73%. We explore several ways to improve aspect robustness, and find that adversarial training can improve models' performance on ARTS by up to 32.85%. Our code and new test set are available at https://github.com/zhijing-jin/ARTS_TestSet
