Emotion-Cause Pair Extraction in Conversations via Semantic Decoupling and Graph Alignment

Researchers propose a semantic decoupling approach to emotion-cause pair extraction in conversations, separating emotion and cause semantics into distinct representation spaces and framing the task as global alignment rather than independent classification. The method aims to capture many-to-many conversational causality more accurately than existing pairwise approaches.

MentionsEmotion-Cause Pair Extraction in Conversations (ECPEC)

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