Abstract
Emotion cause extraction (ECE), the task
aimed at extracting the potential causes behind
certain emotions in text, has gained much attention in recent years due to its wide applications. However, it suffers from two shortcomings: 1) the emotion must be annotated before
cause extraction in ECE, which greatly limits
its applications in real-world scenarios; 2) the
way to first annotate emotion and then extract
the cause ignores the fact that they are mutually indicative. In this work, we propose a new
task: emotion-cause pair extraction (ECPE),
which aims to extract the potential pairs of emotions and corresponding causes in a document. We propose a 2-step approach to address this new ECPE task, which first performs individual emotion extraction and cause extraction via multi-task learning, and then conduct emotion-cause pairing and filtering. The
experimental results on a benchmark emotion
cause corpus prove the feasibility of the ECPE
task as well as the effectiveness of our approach