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arXiv · 2503.06899

KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus

Xiaoming Shi·Zeming Liu·Yiming Lei·Chenkai Zhang·Haitao Leng·Chuan Wang·Qingjie Liu·Wanxiang Che·Shaoguo Liu·Size Li·Yunhong Wang
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Abstract

Video-based dialogue systems, such as education assistants, have compelling application value, 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, emotional 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.

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Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang, Haitao Leng, Chuan Wang, Qingjie Liu, Wanxiang Che, Shaoguo Liu, Size Li, Yunhong Wang. 2025-05-15. KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus. https://arxiv.org/abs/2503.06899

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