Knowledge-Grounded Dialogue Generation with Pre-trained Language Models

Abstract

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 pretrained 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.