EEEC: Emotion-Experiencer-Event-Cause multi-step chain reasoning for Emotion-Cause Pair Extraction
Keywords: Experiencer; Event; Multi-step chain reasoning; Emotion-Cause Pair Extraction
Abstract: Emotion-cause pair extraction (ECPE) aims to identify all emotion and cause clauses in documents, forming the ECPs. Although existing methods have achieved some success, they face issues such as overlooking the impact of emotion experiencers, failing to leverage specific domain knowledge, and tending to spurious correlations. To address these issues, we transform the ECPE task into a multi-step reasoning problem and propose the Emotion-Experience-Event-Cause (EEEC) framework. We introduce an experiencer identification task to understand the source of emotions and enhance the association between emotion and cause clauses. In addition, by combining both prior knowledge and induced reasoning, EEEC guides a large-scale language model (LLM) to perform the emotion-reason pair extraction task efficiently. Experimental results demonstrate that EEEC achieves performance close to current state-of-the-art supervised fine-tuning methods. The data and code are released at https://anonymous.4open.science/r/EEEC-EB80/.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 10933
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