Traditional end-to-end task-oriented dialog systems first convert dialog context into belief state and action state before generating the system response. The system response performance is significantly affected by the quality of the belief state and action state. We first explore what dialog context representation is beneficial to improving the quality of the belief state and action state, which further enhances the generated response quality. To tackle our exploration, we propose Mars, an end-to-end task-oriented dialog system with two contrastive learning strategies to model the relationship between dialog context and belief/action state representations. Empirical results show dialog context representations, which are more different from semantic state representations, are more conducive to multi-turn task-oriented dialog. Moreover, our proposed Mars achieves state-of-the-art performance on the MultiWOZ 2.0, CamRest676, and CrossWOZ.

翻译：传统端到端任务型对话系统首先生成对话上下文的信念状态与动作状态，再产生系统回复。系统回复性能显著受信念状态与动作状态质量的影响。我们首先探索何种对话上下文表征有助于提升信念状态与动作状态的质量，进而增强生成回复的质量。为应对这一探索，我们提出Mars——一种端到端任务型对话系统，采用两种对比学习策略对对话上下文与信念/动作状态表征之间的关系进行建模。实验结果表明，与语义状态表征差异更大的对话上下文表征更有利于多轮任务型对话。此外，我们提出的Mars在MultiWOZ 2.0、CamRest676和CrossWOZ三个数据集上均达到了最先进的性能。