The Emotion Cause Extraction (ECE)} task aims to identify clauses which contain emotion-evoking information for a particular emotion expressed in text. We observe that a widely-used ECE dataset exhibits a bias that the majority of annotated cause clauses are either directly before their associated emotion clauses or are the emotion clauses themselves. Existing models for ECE tend to explore such relative position information and suffer from the dataset bias. To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses. We test the performance of existing models on such adversarial examples and observe a significant performance drop. To address the dataset bias, we propose a novel graph-based method to explicitly model the emotion triggering paths by leveraging the commonsense knowledge to enhance the semantic dependencies between a candidate clause and an emotion clause. Experimental results show that our proposed approach performs on par with the existing state-of-the-art methods on the original ECE dataset, and is more robust against adversarial attacks compared to existing models.

翻译：情感原因抽取任务旨在识别文本中包含特定情感诱发信息的子句。我们发现一个广泛使用的情感原因抽取数据集存在偏差：大多数标注的原因子句要么直接位于其关联情感子句之前，要么就是情感子句本身。现有情感原因抽取模型倾向于利用这种相对位置信息，因此受到数据集偏差的影响。为了探究现有情感原因抽取模型对子句相对位置的依赖程度，我们提出了一种生成对抗样本的新策略，使相对位置信息不再成为原因子句的指示性特征。我们测试了现有模型在这些对抗样本上的性能，并观察到显著的性能下降。为解决数据集偏差问题，我们提出了一种基于图的新方法，通过利用常识知识显式建模情感触发路径，增强候选子句与情感子句之间的语义依赖关系。实验结果表明，我们提出的方法在原始情感原因抽取数据集上表现与现有最先进方法相当，并且与现有模型相比，对对抗攻击具有更强的鲁棒性。