An End-to-End Multi-task Learning Network with Scope Controller for Emotion-Cause Pair Extraction

Abstract

Emotion-cause pair extraction (ECPE) aims to extract all potential pairs of emotions and corresponding causes in a document. It has an advantage over traditional emotion cause extraction (ECE) that it does not require annotating emotions manually. Existing methods for ECPE task are based on two-step framework. However, they ignore the fact that the emotion-cause pair is regarded as a whole unit and there are cascading errors in two-step framework. In this paper, we propose an end-to-end hierarchical neural network model, which directly extracts emotion-cause pairs and enhances mutual interaction between emotions and causes via multi-task learning. In addition, we introduce a scope controller to constrain the emotion-cause pair predictions in a high probability area, according to the position correlation between emotions and causes. The experimental results demonstrate that our method achieves the state-of-the-art performance and improves F-measure by 2.24%.