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Building a universal conversational agent has been a long-standing goal ofthe dialogue research community. Most previous works only focus on a small setof dialogue tasks. In this work, we aim to build a unified dialogue foundationmodel (DFM) which can be used to solve massive diverse dialogue tasks. Toachieve this goal, a large-scale well-annotated dialogue dataset with rich taskdiversity (DialogZoo) is collected. We introduce a framework to unify alldialogue tasks and propose novel auxiliary self-supervised tasks to achievestable training of DFM on the highly diverse large scale DialogZoo corpus.Experiments show that, compared with models of the same size, DFM can achievestate-of-the-art or competitive performance on very rich cross-domaindownstream dialogue tasks. This demonstrates that DFM largely extends theability of unified dialogue pre-trained model.

DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning