  Dialogue state tracking (DST) is an important part of a spoken dialogue
system. Existing DST models either ignore temporal feature dependencies across
dialogue turns or fail to explicitly model temporal state dependencies in a
dialogue. In this work, we propose Temporally Expressive Networks (TEN) to
jointly model the two types of temporal dependencies in DST. The TEN model
utilizes the power of recurrent networks and probabilistic graphical models.
Evaluating on standard datasets, TEN is demonstrated to be effective in
improving the accuracy of turn-level-state prediction and the state
aggregation.
