DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning
This work addresses the challenge of creating a versatile dialogue system for broad conversational AI applications, though it appears incremental as it builds on existing pre-trained model frameworks.
The authors tackled the problem of building a universal conversational agent by developing a unified dialogue foundation model (DFM) trained on a large-scale diverse dataset (DialogZoo), which achieved state-of-the-art or competitive performance on cross-domain dialogue tasks compared to models of similar size.
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.