LiveChat：从直播中自动构建的大规模个性化对话数据集
LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming
AI总结：
针对现有对话系统在直播场景迁移性差的问题，该研究推出含133万条对话、覆盖351个细粒度人设的多方对话数据集LiveChat，提出响应建模与收信人识别的检索基线，验证了人设画像与人均会话量的作用，并测试了生成式模型的迁移性。
摘要：
近年来，开放域对话系统取得了可观的进展。尽管当前最先进的对话智能体基于大规模文本类社交媒体数据与大型预训练模型构建，但由于预训练模型的迁移能力有限，且Reddit、微博等公开数据集存在分布偏差，无法保证这些智能体在直播这类快速发展的场景中也能表现良好。为提升响应核心能力并建立直播开放域场景的基准，我们推出LiveChat数据集，它包含133万条真实中文对话，覆盖351个人设，每个人设平均有近3800个会话，且每个人设都配有细粒度画像。LiveChat通过处理互联网上的大量直播视频自动构建，天然属于多方对话范畴，需要考虑“谁对谁说了什么”的问题。因此，我们针对响应建模与收信人识别两项关键任务，基于先进技术提出了基于检索的基线方法。实验结果证实了利用人设画像以及提升每个人设的平均会话数量具有积极效果。此外，我们还在LiveChat上对先进的生成式模型的迁移能力进行了基准测试，并针对当前挑战提出了若干未来研究方向。
英文摘要：
Open-domain dialogue systems have made promising progress in recent years. While the state-of-the-art dialogue agents are built upon large-scale text-based social media data and large pre-trained models, there is no guarantee these agents could also perform well in fast-growing scenarios, such as live streaming, due to the bounded transferability of pre-trained models and biased distributions of public datasets from Reddit and Weibo, etc. To improve the essential capability of responding and establish a benchmark in the live open-domain scenario, we introduce the LiveChat dataset, composed of 1.33 million real-life Chinese dialogues with almost 3800 average sessions across 351 personas and fine-grained profiles for each persona. LiveChat is automatically constructed by processing numerous live videos on the Internet and naturally falls within the scope of multi-party conversations, where the issues of Who says What to Whom should be considered. Therefore, we target two critical tasks of response modeling and addressee recognition and propose retrieval-based baselines grounded on advanced techniques. Experimental results have validated the positive effects of leveraging persona profiles and larger average sessions per persona. In addition, we also benchmark the transferability of advanced generation-based models on LiveChat and pose some future directions for current challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
