DFM: Dialogue Foundation Model For Universal Large-scale Dialogue-oriented Task Learning
Chen Zhi, Bao Jijia, Chen Lu, Liu Yuncong, Ma Da, Chen Bei, Wu Mengyue, Zhu Su, Dong Xin, Ge Fujiang, Miao Qingliang, Lou Jian-guang, Yu Kai. Arxiv 2022
[Paper]
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.
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