@inproceedings{yu-etal-2025-beyond-verbal,
    title = "Beyond Verbal Cues: Emotional Contagion Graph Network for Causal Emotion Entailment",
    author = "Yu, Fangxu  and
      Guo, Junjie  and
      Wu, Zhen  and
      Dai, Xinyu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.findings-acl.88/",
    doi = "10.18653/v1/2025.findings-acl.88",
    pages = "1755--1767",
    ISBN = "979-8-89176-256-5",
    abstract = "Emotions are fundamental to conversational understanding. While significant advancements have been achieved in conversational emotion recognition and emotional response generation, recognizing the causes of eliciting emotions is less explored. Previous studies have primarily focused on identifying the causes of emotions by understanding verbal contextual utterances, overlooking that non-verbal emotional cues can elicit emotions. To address this issue, we develop an Emotional Contagion Graph Network (ECGN) that simulates the impact of non-verbal implicit emotions on the counterpart{'}s emotions. To achieve this, we construct a heterogeneous graph that simulates the transmission of non-verbal emotions alongside verbal influences. By applying message passing between nodes, the constructed graph effectively models both the implicit emotional dynamics and explicit verbal interactions. We evaluate ECGN{'}s performance through extensive experiments on the benchmark datasets and compare it against multiple state-of-the-art models. Experimental results demonstrate the effectiveness of the proposed model. Our code is available at https://github.com/Yu-Fangxu/ECGN."
}