  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.
