Context-Aware Multi-View Attention Networks for Emotion Cause Extraction

Emotion cause extraction aims at automatically identifying cause clauses for a certain emotion expressed in a document. It is an important task in emotion analysis since it helps form a deeper understanding of emotion text. Detecting potential causes of user emotion in online contents is beneficial to public opinion monitoring, government decision-making, and other security-related applications. Existing studies treat this task as a binary clause-level classification problem, which considers each clause separately and omits the context information of clauses. Moreover, previous work only models emotion-dependent linguistic representations of clauses but ignores emotion-independent features in clauses including cause indicators. To address the above two issues, we formalize this task as a sequence labeling problem and propose the COntext-aware Multi-View attention networks (COMV) for emotion cause extraction. Our proposed model integrates context information and learns multi-view clause representations. Experimental results show that our model outperforms existing state-of-the-art methods.