@inproceedings{fu-etal-2026-jopr,
    title = "{J}o{PR}: Joint Emotion Perception and Reasoning for Conversational Emotion Recognition",
    author = "Fu, Yumeng  and
      Huang, Weitao  and
      Wu, Junjie  and
      Teng, Hao  and
      Zhang, Meishan  and
      Liu, Bingquan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1800/",
    pages = "38852--38864",
    ISBN = "979-8-89176-390-6",
    abstract = "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."
}