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Title: Non-Autoregressive Dialog State Tracking

Paper content:
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 \textit{(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.
In task-oriented dialogues, a dialogue agent is required to assist humans for one or many tasks such as finding a restaurant and booking a hotel.
As a sample dialogue shown in Table 1 , each user utterance typically contains important information identified as slots related to a dialogue domain such as attraction-area and train-day.
A crucial part of a task-oriented dialogue system is Dialogue State Tracking (DST), which aims to identify user goals expressed during a conversation in the form of dialogue states.
A dialogue state consists of a set of (slot, value) pairs e.g. (attraction-area, centre) and (train-day, tuesday).
Existing DST models can be categorized into two types: fixed-and open-vocabulary.
Fixed vocabulary models assume known slot ontology and generate a score for each candidate of (slot,value) Lee et al., 2019) .
Recent approaches propose open-vocabulary models that can generate the candidates, especially for slots such as entity names and time, from the dialogue history (Lei et al., 2018; Wu et al., 2019) .
Most open-vocabulary DST models rely on autoregressive encoders and decoders, which encode dialogue history sequentially and generate token t i of individual slot value one by one conditioned on all previously generated tokens t [1:i−1] .
For downstream tasks of DST that emphasize on low latency (e.g. generating real-time dialogue responses), auto-regressive approaches incur expensive time cost as the ongoing dialogues become more complex.
The time cost is caused by two major components: length of dialogue history i.e. number of turns, and length of slot values.
For complex dialogues extended over many turns and multiple domains, the time cost will increase significantly in both encoding and decoding phases.
Similar problems can be seen in the field of Neural Machine Translation (NMT) research where a long piece of text is translated from one language to another.
Recent work has tried to improve the
We proposed NADST, a novel Non-Autoregressive neural architecture for DST that allows the model to explicitly learn dependencies at both slot-level and token-level to improve the joint accuracy rather than just individual slot accuracy.
Our approach also enables fast decoding of dialogue states by adopting a parallel decoding strategy in decoding components.
Our extensive experiments on the well-known MultiWOZ corpus for large-scale multi-domain dialogue systems benchmark show that our NADST model achieved the state-of-the-art accuracy results for DST tasks, while enjoying a substantially low inference latency which is an order of magnitude lower than the prior work.
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assistant: text: A new model for the DST task that reduces inference time complexity with a non-autoregressive decoder, obtains competitive DST accuracy, and shows improvements over other baselines.