A Position-Aware Word-Level and Clause-Level Attention Network for Emotion Cause Recognition

Yufeng Diao, Liang Yang, Xiaochao Fan, Hongfei Lin

Abstract: Emotion cause recognition is a vital task in natural language processing (NLP), which aims to identify the reason of emotion expressed in text. Both industry and academia have realized the importance of the relationship between emotion word and context. However, most existing methods usually ignore the fact that the position information is also crucial for detecting the emotion cause. When an emotion word occurs in a clause, its neighboring words and clauses should be given more attractive than others with a long distance. In this paper, we propose a novel framework Position-aware Word-level and Clause-level Attention (PWCA) Network based on bidirectional GRU. PWCA not only concentrates on the position information of emotion word, but also builds the relation between emotion clause and candidate clause by leveraging word-level and clause-level attention mechanism. The experimental results show that our model obviously outperforms other state-of-the-art methods. Through the visualization of attention over words, we validate our observation mentioned above.