  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.
