Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction

• URL: http://arxiv.org/abs/2208.13549v2
• Date: Tue, 30 Aug 2022 00:57:43 GMT
• Title: Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction
• Authors: Hang Chen, Xinyu Yang, Xiang Li
• Abstract summary: We propose a novel clause-level encoding model named EA-GAT. E-GAT is designed to aggregate information from different types of clauses. We show that our approach has a significant advantage over all current approaches on the Chinese and English benchmark corpus.
• Score: 20.850786410336216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion-cause pair extraction (ECPE) is an emerging task aiming to extract potential pairs of emotions and corresponding causes from documents. Previous approaches have focused on modeling the pair-to-pair relationship and achieved promising results. However, the clause-to-clause relationship, which fundamentally symbolizes the underlying structure of a document, has still been in its research infancy. In this paper, we define a novel clause-to-clause relationship. To learn it applicably, we propose a general clause-level encoding model named EA-GAT comprising E-GAT and Activation Sort. E-GAT is designed to aggregate information from different types of clauses; Activation Sort leverages the individual emotion/cause prediction and the sort-based mapping to propel the clause to a more favorable representation. Since EA-GAT is a clause-level encoding model, it can be broadly integrated with any previous approach. Experimental results show that our approach has a significant advantage over all current approaches on the Chinese and English benchmark corpus, with an average of $2.1\%$ and $1.03\%$.

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