In task-oriented dialogue systems, Dialogue State Tracking (DST) aims to extract users' intentions from the dialogue history. Currently, most existing approaches suffer from error propagation and are unable to dynamically select relevant information when utilizing previous dialogue states. Moreover, the relations between the updates of different slots provide vital clues for DST. However, the existing approaches rely only on predefined graphs to indirectly capture the relations. In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing. Thus, it can dynamically exploit previous dialogue states and avoid introducing error propagation simultaneously. Further, we propose an inter-slot contrastive learning loss to effectively capture the slot co-update relations from dialogue context. Experiments are conducted on the widely used MultiWOZ 2.0 and MultiWOZ 2.1 datasets. The experimental results show that our proposed model achieves the state-of-the-art performance for DST.

翻译：在任务导向型对话系统中，对话状态跟踪旨在从对话历史中提取用户意图。当前，大多数现有方法存在错误传播问题，且在利用先前对话状态时无法动态选择相关信息。此外，不同插槽更新之间的关系为对话状态跟踪提供了关键线索，但现有方法仅依赖预定义图来间接捕获这些关系。本文提出一种对话状态蒸馏网络，通过利用先前对话状态的相关信息，弥合训练与测试阶段的信息利用差异，从而既能动态利用先前对话状态，又能避免引入错误传播。进一步地，我们提出一种插槽间对比学习损失函数，用以从对话上下文中有效捕获插槽的协同更新关系。在广泛使用的MultiWOZ 2.0和MultiWOZ 2.1数据集上进行的实验表明，所提模型在对话状态跟踪任务上取得了最先进的性能。