A Multi-Task Learning Neural Network for Emotion-Cause Pair Extraction
Sixing Wu, Fang Chen, Fangzhao Wu, Yongfeng Huang, Xing Li
Abstract
Emotion-cause pair extraction, which aims at extracting both the emotion and its corresponding cause in text, is a significant and challenging task in emotion analysis. Previous work formulated the task in a two-step framework, i.e., emotion and cause extraction, and emotion-cause relation classification. However, different tasks may correlate with each other and the two-step framework does not fully exploit the interactions between tasks. In this paper, we propose a multi-task neural network to perform emotion-cause pair extraction in a unified model. The task of relation classification is learned together with emotion and cause extraction. To this end, we develop a method to obtain training samples for relation classification without the dependence on the result of emotion and cause extraction. To fully exploit the interactions between different tasks, our model shares useful features across tasks. Moreover, we propose a method to incorporate position-aware emotion information in cause extraction to further improve the performance. Experimental results show that our model outperforms the state-of-the-art model on emotion-cause pair extraction.