  Building a universal conversational agent has been a long-standing goal of
the dialogue research community. Most previous works only focus on a small set
of dialogue tasks. In this work, we aim to build a unified dialogue foundation
model (DFM) which can be used to solve massive diverse dialogue tasks. To
achieve this goal, a large-scale well-annotated dialogue dataset with rich task
diversity (DialogZoo) is collected. We introduce a framework to unify all
dialogue tasks and propose novel auxiliary self-supervised tasks to achieve
stable training of DFM on the highly diverse large scale DialogZoo corpus.
Experiments show that, compared with models of the same size, DFM can achieve
state-of-the-art or competitive performance on very rich cross-domain
downstream dialogue tasks. This demonstrates that DFM largely extends the
ability of unified dialogue pre-trained model.
