@inproceedings{wu-etal-2025-multimodal,
    title = "Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects",
    author = "Wu, ChengYan  and
      Cai, Yiqiang  and
      Liu, Yang  and
      Zhu, Pengxu  and
      Xue, Yun  and
      Gong, Ziwei  and
      Hirschberg, Julia  and
      Ma, Bolei",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.332/",
    doi = "10.18653/v1/2025.findings-emnlp.332",
    pages = "6257--6274",
    ISBN = "979-8-89176-335-7",
    abstract = "While text-based emotion recognition methods have achieved notable success, real-world dialogue systems often demand a more nuanced emotional understanding than any single modality can offer. Multimodal Emotion Recognition in Conversations (MERC) has thus emerged as a crucial direction for enhancing the naturalness and emotional understanding of human-computer interaction. Its goal is to accurately recognize emotions by integrating information from various modalities such as text, speech, and visual signals. This survey offers a systematic overview of MERC, including its motivations, core tasks, representative methods, and evaluation strategies. We further examine recent trends, highlight key challenges, and outline future directions. As interest in emotionally intelligent systems grows, this survey provides timely guidance for advancing MERC research."
}