Why Do Emotions Change? Appraisal-Guided Reasoning for Emotion–Cause Triplet Extraction in Conversations
Qiao Liang, Ying Shen Tongji University, Yao Liu, Tiantian Chen, Lin Zhang IEIT SYSTEMS (Beijing) Co., Ltd. and Tongji University
Multimodal Emotion–Cause Triplet Extraction in Conversations (MECTEC) is fundamental for fine-grained affect understanding, yet it remains challenging in multi-turn, multi-speaker settings. Existing methods often make locally plausible predictions but struggle to maintain conversation-level consistency under within-speaker emotion shifts and core events. To address this, we propose ECFlow, a unified framework that combines appraisal-guided structured generation with graph-structured reinforcement learning. ECFlow operationalizes cognitive appraisal theory into a controllable intermediate reasoning trace and constructs UMECS, a unified supervision dataset with cognitively grounded traces. It then lifts predicted and gold triplets into an Emotion–Cause Flow Graph and optimizes verifiable, structure-aware rewards for emotion-shift coherence and core-event consistency, together with task-oriented triplet rewards. Experiments on public MECTEC benchmarks show that ECFlow consistently outperforms strong baselines, achieving state-of-the-art triplet extraction and improved structure-aware metrics on emotion shifts and core events. Our code and dataset are available at https://anonymous.4open.science/r/ECFlow-E908.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表