@inproceedings{sun-etal-2026-emotiontalk,
    title = "{E}motion{T}alk: An Interactive {C}hinese Multimodal Emotion Dataset With Rich Annotations",
    author = "Sun, Haoqin  and
      Zhao, Jinghua  and
      Wang, Xuechen  and
      Zhao, Shiwan  and
      Zhou, Jiaming  and
      Wang, Hui  and
      Yang, Xi  and
      Wang, Yequan  and
      Lin, Yonghua",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.440/",
    pages = "9054--9071",
    ISBN = "979-8-89176-395-1",
    abstract = "The advancement of Multimodal Emotion Recognition (MER) in Chinese is significantly hindered by the scarcity of high-quality, spontaneous dialogue datasets compared to their English counterparts. In this work, we introduce EmotionTalk, the first interactive Chinese multimodal dataset designed to capture the nuance of authentic emotional interplay. Collected from 19 professional actors, the dataset spans 23.6 hours of dyadic conversations across diverse scenarios. A key contribution of EmotionTalk is its multi-grained annotation system, which integrates standard categorical and dimensional labels with fine-grained emotional speaking style captions, enabling research into interpretable emotion analysis. We establish comprehensive benchmarks for emotion recognition and captioning tasks, verifying the dataset{'}s effectiveness and the necessity of multimodal fusion. EmotionTalk serves as a critical resource for bridging the gap in non-English affective computing and is publicly released for the research community."
}