Non-Autoregressive Dialog State Tracking
Hung Le, Richard Socher, Steven C.H. Hoi

TL;DR
This paper introduces NADST, a non-autoregressive framework for dialogue state tracking that models dependencies among slots and domains, significantly reducing latency while achieving state-of-the-art accuracy on MultiWOZ 2.1.
Contribution
The paper proposes a novel non-autoregressive approach for DST that captures slot and domain dependencies and enables parallel decoding, improving efficiency and accuracy.
Findings
Achieves state-of-the-art joint accuracy on MultiWOZ 2.1.
Reduces DST latency by an order of magnitude.
Effectively models dependencies among slots and domains.
Abstract
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…
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Taxonomy
TopicsTopic Modeling · Speech and dialogue systems · Natural Language Processing Techniques
MethodsDynamic Sparse Training
