  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.
