@inproceedings{shi-etal-2025-kwaichat,
    title = "{K}wai{C}hat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus",
    author = "Shi, Xiaoming  and
      Liu, Zeming  and
      Lei, Yiming  and
      Zhang, Chenkai  and
      Leng, Haitao  and
      Wang, Chuan  and
      Liu, Qingjie  and
      Che, Wanxiang  and
      Wang, Yunhong",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.121/",
    pages = "2279--2294",
    ISBN = "979-8-89176-195-7",
    abstract = "Video-based dialogue systems have compelling application value, such as education assistants, thereby garnering growing interest. However, the current video-based dialogue systems are limited by their reliance on a single dialogue type, which hinders their versatility in practical applications across a range of scenarios, including question-answering and emotionally dialog, etc. In this paper, we identify this challenge as how to generate video-driven multilingual mixed-type dialogues. To mitigate this challenge, we propose a novel task and create a human-to-human video-driven multilingual mixed-type dialogue corpus, termed KwaiChat, containing a total of 93,209 videos and 246,080 dialogues, across 4 dialogue types, 30 domains, 4 languages, and 13 topics. Additionally, we establish baseline models on KwaiChat. An extensive analysis of 7 distinct LLMs on KwaiChat reveals that GPT-4o achieves the best performance but still cannot perform well in this situation even with the help of in-context learning and fine-tuning, which indicates that the task is not trivial and needs further research."
}