@inproceedings{chang-etal-2026-multimodal,
    title = "Multimodal Conversation Structure Understanding",
    author = "Chang, Kent K.  and
      Cramer, Mackenzie Hanh  and
      Ho, Anna  and
      Nguyen, Ti Ti  and
      Yuan, Yilin  and
      Bamman, David",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.349/",
    pages = "7437--7458",
    ISBN = "979-8-89176-380-7",
    abstract = "While multimodal large language models (LLMs) excel at dialogue, whether they can adequately parse the structure of conversation{---}conversational roles and threading{---}remains underexplored. In this work, we introduce a suite of tasks and release TV-MMPC, a new annotated dataset, for multimodal conversation structure understanding. Our evaluation reveals that while all multimodal LLMs outperform our heuristic baseline, even the best-performing model we consider experiences a substantial drop in performance when character identities of the conversation are anonymized. Beyond evaluation, we carry out a sociolinguistic analysis of 350,842 utterances in TVQA. We find that while female characters initiate conversations at rates in proportion to their speaking time, they are 1.2 times more likely than men to be cast as an addressee or side-participant, and the presence of side-participants shifts the conversational register from personal to social."
}