  Existing pipelined task-oriented dialogue systems usually have difficulties
adapting to unseen domains, whereas end-to-end systems are plagued by
large-scale knowledge bases in practice. In this paper, we introduce a novel
query-driven task-oriented dialogue system, namely Q-TOD. The essential
information from the dialogue context is extracted into a query, which is
further employed to retrieve relevant knowledge records for response
generation. Firstly, as the query is in the form of natural language and not
confined to the schema of the knowledge base, the issue of domain adaption is
alleviated remarkably in Q-TOD. Secondly, as the query enables the decoupling
of knowledge retrieval from the generation, Q-TOD gets rid of the issue of
knowledge base scalability. To evaluate the effectiveness of the proposed
Q-TOD, we collect query annotations for three publicly available task-oriented
dialogue datasets. Comprehensive experiments verify that Q-TOD outperforms
strong baselines and establishes a new state-of-the-art performance on these
datasets.
