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

2026-04-21 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

A new framework called SCALE has been developed for Emotion-Cause Pair Extraction in Conversations (ECPEC), a task focused on identifying causal relationships between emotion utterances and their triggers in dialogues. Existing ECPEC methods often treat this as an independent pairwise classification, which overlooks the distinct semantics of emotion diffusion and cause explanation, and struggles with globally consistent many-to-many conversational causality. SCALE addresses these issues by decoupling emotion-oriented and cause-oriented semantics into two complementary representation spaces. It then formulates ECPEC as a global alignment problem between these representations, utilizing optimal transport for many-to-many and globally consistent emotion-cause matching. Experiments on benchmark datasets show that SCALE consistently achieves state-of-the-art performance.

Key takeaway

For research scientists developing conversational AI systems, understanding the limitations of pairwise classification for emotion-cause extraction is crucial. You should consider adopting semantic decoupling and global alignment techniques, as demonstrated by the SCALE framework, to achieve more accurate and contextually consistent identification of emotional triggers in dialogue. This approach can significantly enhance the robustness of your models in complex conversational settings.

Key insights

Decoupling emotion and cause semantics improves conversational emotion-cause pair extraction.

Principles

• Emotion and cause semantics are distinct.
• Global alignment improves many-to-many matching.

Method

SCALE disentangles emotion-oriented and cause-oriented semantics into separate representation spaces, then uses optimal transport for global alignment to match emotion-cause pairs.

In practice

• Apply optimal transport for global matching.
• Separate distinct semantic roles in NLP tasks.

Topics

• Emotion-Cause Pair Extraction
• Conversational AI
• Semantic Decoupling
• Graph Alignment
• Optimal Transport

Code references

• CoCoSphere/SCALE

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.