@inproceedings{mao-etal-2021-dialoguetrm-exploring,
    title = "{D}ialogue{TRM}: Exploring Multi-Modal Emotional Dynamics in a Conversation",
    author = "Mao, Yuzhao  and
      Liu, Guang  and
      Wang, Xiaojie  and
      Gao, Weiguo  and
      Li, Xuan",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.findings-emnlp.229/",
    doi = "10.18653/v1/2021.findings-emnlp.229",
    pages = "2694--2704",
    abstract = "Emotion dynamics formulates principles explaining the emotional fluctuation during conversations. Recent studies explore the emotion dynamics from the self and inter-personal dependencies, however, ignoring the temporal and spatial dependencies in the situation of multi-modal conversations. To address the issue, we extend the concept of emotion dynamics to multi-modal settings and propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics. Specifically, the intra-modal emotion dynamics is to not only capture the temporal dependency but also satisfy the context preference in every single modality. The inter-modal emotional dynamics aims at handling multi-grained spatial dependency across all modalities. Our models outperform the state-of-the-art with a margin of 4{\%}-16{\%} for most of the metrics on three benchmark datasets."
}