  In this paper, we propose an unsupervised query enhanced approach for
knowledge-intensive conversations, namely QKConv. There are three modules in
QKConv: a query generator, an off-the-shelf knowledge selector, and a response
generator. QKConv is optimized through joint training, which produces the
response by exploring multiple candidate queries and leveraging corresponding
selected knowledge. The joint training solely relies on the dialogue context
and target response, getting exempt from extra query annotations or knowledge
provenances. To evaluate the effectiveness of the proposed QKConv, we conduct
experiments on three representative knowledge-intensive conversation datasets:
conversational question-answering, task-oriented dialogue, and
knowledge-grounded conversation. Experimental results reveal that QKConv
performs better than all unsupervised methods across three datasets and
achieves competitive performance compared to supervised methods.
