Abstract: 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.