Therefore, we propose an unsupervised approach where learning of knowledge selection and fine-tuning of response generation are jointly conducted with unlabeled dialogues.
we build the knowledge selection module on the basis of BERT, and formalize knowledge selection as a sequence prediction process, by which the model can take advantage of the pre-training techniques and dynamically determine the relevant knowledge for a given context.