The research of knowledge-driven conversational systems is largely limited
due to the lack of dialog data which consist of multi-turn conversations on
multiple topics and with knowledge annotations. In this paper, we propose a
Chinese multi-domain knowledge-driven conversation dataset, KdConv, which
grounds the topics in multi-turn conversations to knowledge graphs. Our corpus
contains 4.5K conversations from three domains (film, music, and travel), and
86K utterances with an average turn number of 19.0. These conversations contain
in-depth discussions on related topics and natural transition between multiple
topics. To facilitate the following research on this corpus, we provide several
benchmark models. Comparative results show that the models can be enhanced by
introducing background knowledge, yet there is still a large space for
leveraging knowledge to model multi-turn conversations for further research.
Results also show that there are obvious performance differences between
different domains, indicating that it is worth to further explore transfer
learning and domain adaptation. The corpus and benchmark models are publicly
available.

本文提出了一个中文多领域知识驱动会话数据集，该数据集将多轮会话的话题与知识图谱相匹配。我们的语料库包含三个领域（电影，音乐和旅游）的 4.5K 个会话，86K 个话语，平均轮数为 19.0。我们提供了多个基准模型来促进基于该语料库的后续研究。比较结果表明，通过引入背景知识，可以增强模型，但仍有大量空间可用于利用知识来模拟多轮会话以进行进一步研究。结果还表明，不同领域之间存在明显的性能差异，这表明值得进一步探索转移学习和领域适应。该语料库和基准模型是公开可用的。