论文标题
DFM：通用大规模对话任务学习的对话基础模型
DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning
论文作者
论文摘要
建立通用的对话代理一直是对话研究社区的长期目标。以前的大多数作品仅着眼于一小部分对话任务。在这项工作中，我们旨在建立一个统一的对话基础模型（DFM），该模型可用于解决庞大的多样化对话任务。为了实现这一目标，收集了具有丰富任务多样性（Dialogzoo）的大规模宣布的对话数据集。我们介绍了一个框架，以统一所有对话任务，并提出新颖的辅助自我监督任务，以在高度多样化的大规模对话过程中对DFM进行稳定的培训。实验表明，与相同尺寸的模型相比，DFM可以在非常丰富的跨域下游对话任务上实现最先进或竞争性能。这表明DFM在很大程度上扩展了统一对话预训练模型的能力。
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.
