  Knowledge-grounded dialogue is a task of generating a fluent and informative
response based on both conversation context and a collection of external
knowledge, in which knowledge selection plays an important role and attracts
more and more research interest. However, most existing models either select
only one knowledge or use all knowledge for responses generation. The former
may lose valuable information in discarded knowledge, while the latter may
bring a lot of noise. At the same time, many approaches need to train the
knowledge selector with knowledge labels that indicate ground-truth knowledge,
but these labels are difficult to obtain and require a large number of manual
annotations. Motivated by these issues, we propose Knoformer, a dialogue
response generation model based on reinforcement learning, which can
automatically select one or more related knowledge from the knowledge pool and
does not need knowledge labels during training. Knoformer is evaluated on two
knowledge-guided conversation datasets, and achieves state-of-the-art
performance.
