← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1800 · 记录 #1
JoPR: Joint Emotion Perception and Reasoning for Conversational Emotion Recognition
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:48
摘要
Emotion Recognition in Conversation (ERC), the task of identifying the emotion of each utterance in a conversation, is crucial for human-machine interaction. Existing LLM-based ERC methods focus on standard prompting and slow thinking for emotion analysis. However, they suffer from the lack of human-like emotion reasoning and discrimination between similar emotions, thus limiting accurate emotion predictions. To this end, we present JoPR, jointing perception-curriculum learning and emotional reasoning for conversational emotion recognition. Specifically, we devise a multi-dimension curriculum with long CoT fine-tuning to clone human-like emotion reasoning. We further design an emotion-specific reward function in a novel reinforcement learning framework, thereby enhancing the discernment between similar emotions. Our proposal is extensively evaluated over three widely used benchmark datasets, and experimental results confirm the superiority of JoPR. Furthermore, we provide an in-depth analysis to confirm the emotion perception and reasoning capabilities of JoPR.
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记录 #1 · 2026-07 · 最新观测版本
JoPR: Joint Emotion Perception and Reasoning for Conversational Emotion Recognition
Emotion Recognition in Conversation (ERC), the task of identifying the emotion of each utterance in a conversation, is crucial for human-machine interaction. Existing LLM-based ERC methods focus on standard prompting and slow thinking for emotion analysis. However, they suffer from the lack of human-like emotion reasoning and discrimination between similar emotions, thus limiting accurate emotion predictions. To this end, we present JoPR, jointing perception-curriculum learning and emotional reasoning for conversational emotion recognition. Specifically, we devise a multi-dimension curriculum with long CoT fine-tuning to clone human-like emotion reasoning. We further design an emotion-specific reward function in a novel reinforcement learning framework, thereby enhancing the discernment between similar emotions. Our proposal is extensively evaluated over three widely used benchmark datasets, and experimental results confirm the superiority of JoPR. Furthermore, we provide an in-depth analysis to confirm the emotion perception and reasoning capabilities of JoPR.
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