  We study knowledge-grounded dialogue generation with pre-trained language
models. To leverage the redundant external knowledge under capacity constraint,
we propose equipping response generation defined by a pre-trained language
model with a knowledge selection module, and an unsupervised approach to
jointly optimizing knowledge selection and response generation with unlabeled
dialogues. Empirical results on two benchmarks indicate that our model can
significantly outperform state-of-the-art methods in both automatic evaluation
and human judgment.
