  An indispensable component in task-oriented dialogue systems is the dialogue
state tracker, which keeps track of users' intentions in the course of
conversation. The typical approach towards this goal is to fill in multiple
pre-defined slots that are essential to complete the task. Although various
dialogue state tracking methods have been proposed in recent years, most of
them predict the value of each slot separately and fail to consider the
correlations among slots. In this paper, we propose a slot self-attention
mechanism that can learn the slot correlations automatically. Specifically, a
slot-token attention is first utilized to obtain slot-specific features from
the dialogue context. Then a stacked slot self-attention is applied on these
features to learn the correlations among slots. We conduct comprehensive
experiments on two multi-domain task-oriented dialogue datasets, including
MultiWOZ 2.0 and MultiWOZ 2.1. The experimental results demonstrate that our
approach achieves state-of-the-art performance on both datasets, verifying the
necessity and effectiveness of taking slot correlations into consideration.
