Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts
This addresses the problem of inefficient emotion analysis in real-world scenarios for NLP researchers and practitioners by eliminating the need for pre-annotated emotions, though it is incremental as it builds on existing ECE work.
The paper tackles the limitation of emotion cause extraction (ECE) by proposing a new task called emotion-cause pair extraction (ECPE), which extracts pairs of emotions and their causes directly from text without prior emotion annotation, and demonstrates its feasibility with experimental results on a benchmark corpus.
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.