  Compared with CrossWOZ (Chinese) and MultiWOZ (English) dataset which have
coarse-grained information, there is no dataset which handle fine-grained and
hierarchical level information properly. In this paper, we publish a first
Cantonese knowledge-driven Dialogue Dataset for REStaurant (KddRES) in Hong
Kong, which grounds the information in multi-turn conversations to one specific
restaurant. Our corpus contains 0.8k conversations which derive from 10
restaurants with various styles in different regions. In addition to that, we
designed fine-grained slots and intents to better capture semantic information.
The benchmark experiments and data statistic analysis show the diversity and
rich annotations of our dataset. We believe the publish of KddRES can be a
necessary supplement of current dialogue datasets and more suitable and
valuable for small and middle enterprises (SMEs) of society, such as build a
customized dialogue system for each restaurant. The corpus and benchmark models
are publicly available.
