A Hierarchical Inter-Clause Interaction Network for Emotion Cause Extraction

Peiqin Lin, Meng Yang, Yu Gu

Abstract: Recently, some methods with inter-clause interaction have achieved promising results on the task of emotion cause extraction. However, the inter-clause modeling modules are only applied on clause-level features rather than word-level features, thus weakening the ability to capture important word-level cues for identifying whether a clause is an emotion cause. In this paper, we propose a framework of Hierarchical Inter-Clause Interaction Network (HICIN), in which inter-clause interaction is applied on both word-level and clause-level features. Word-level interaction can capture the fine-grained semantic cues of each clause by the guidance of all clauses in the document and then generate more powerful clause-level features, while clause-level interaction makes the obtained clause-level features more discriminative. Experimental results show that our model can improve the performance effectively.