  Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues
have progressed toward open-vocabulary or generation-based approaches where the
models can generate slot value candidates from the dialogue history itself.
These approaches have shown good performance gain, especially in complicated
dialogue domains with dynamic slot values. However, they fall short in two
aspects: (1) they do not allow models to explicitly learn signals across
domains and slots to detect potential dependencies among (domain, slot) pairs;
and (2) existing models follow auto-regressive approaches which incur high time
cost when the dialogue evolves over multiple domains and multiple turns. In
this paper, we propose a novel framework of Non-Autoregressive Dialog State
Tracking (NADST) which can factor in potential dependencies among domains and
slots to optimize the models towards better prediction of dialogue states as a
complete set rather than separate slots. In particular, the non-autoregressive
nature of our method not only enables decoding in parallel to significantly
reduce the latency of DST for real-time dialogue response generation, but also
detect dependencies among slots at token level in addition to slot and domain
level. Our empirical results show that our model achieves the state-of-the-art
joint accuracy across all domains on the MultiWOZ 2.1 corpus, and the latency
of our model is an order of magnitude lower than the previous state of the art
as the dialogue history extends over time.
