Emotion–Cause Pair Extraction in Conversations via Semantic Decoupling and Alignment

ACL ARR 2026 January Submission1718 Authors

Keywords: Emotion–Cause Pair Extraction, Conversational Emotion Analysis, Graph Alignment, Semantic Decoupling

Abstract:

Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering causes within a dialogue.
Most existing approaches formulate ECPEC as independent pairwise classification, overlooking the distinct semantics of emotion diffusion and cause explanation, and failing to capture globally consistent many-to-many conversational causality.
To address these limitations, we revisit ECPEC from a semantic perspective and seek to disentangle emotion-oriented semantics from cause-oriented semantics, mapping them into two complementary representation spaces to better capture their distinct conversational roles.
Building on this semantic decoupling, we naturally formulate ECPEC as a global alignment problem between the emotion-side and cause-side representations, and employ optimal transport to enable many-to-many and globally consistent emotion-cause matching.
Based on this perspective, we propose a unified framework SCALE that instantiates the above semantic decoupling and alignment principle within a shared conversational structure.
Extensive experiments on several benchmark datasets demonstrate that SCALE consistently achieves state-of-the-art performance.

Paper Type: Long

Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Research Area Keywords: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Contribution Types: NLP engineering experiment, Data analysis

Languages Studied: English

Submission Number: 1718