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.

本文提出一种 Dialogue State Distillation Network（DSDN）用于进行任务导向的对话系统中的 Dialogue State Tracking（DST），并且在 MultiWOZ 2.0 和 MultiWOZ 2.1 数据集上实验证明，该网络可以动态地利用先前的对话状态并避免引入误差传播，同时还可以有效地捕捉来自对话语境的插槽共同更新关系