Non-Autoregressive Dialog State Tracking

1 INTRODUCTION . 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 ) ( Ramadan et al. , 2018 ; 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 ti 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 ∗All work was done while the first author was a research intern at Salesforce Research Asia . latency in NMT by using neural network architectures such as convolution ( Krizhevsky et al. , 2012 ) and attention ( Luong et al. , 2015 ) . Several non- and semi-autoregressive approaches aim to generate tokens of the target language independently ( Gu et al. , 2018 ; Lee et al. , 2018 ; Kaiser et al. , 2018 ) . Motivated by this line of research , we thus propose a non-autoregressive approach to minimize the time cost of DST models without a negative impact on the model performance . We adopt the concept of fertility proposed by Gu et al . ( 2018 ) . Fertility denotes the number of times each input token is copied to form a sequence as the input to the decoder for non-autoregressive decoding . We first reconstruct dialogue state as a sequence of concatenated slot values . The result sequence contains the inherent structured representation in which we can apply the fertility concept . The structure is defined by the boundaries of individual slot values . These boundaries can be easily obtained from dialogue state itself by simply measuring number of the tokens of individual slots . Our model includes a two-stage decoding process : ( 1 ) the first decoder learns relevant signals from the input dialogue history and generates a fertility for each input slot representation ; and ( 2 ) the predicted fertility is used to form a structured sequence which consists of multiple sub-sequences , each represented as ( slot token×slot fertility ) . The result sequence is used as input to the second decoder to generate all the tokens of the target dialogue state at once . In addition to being non-autoregressive , our models explicitly consider dependencies at both slot level and token level . Most of existing DST models assume independence among slots in dialogue states without explicitly considering potential signals across the slots ( Wu et al. , 2019 ; Lee et al. , 2019 ; Goel et al. , 2019 ; Gao et al. , 2019 ) . However , we hypothesize that it is not true in many cases . For example , a good DST model should detect the relation that train departure should not have the same value as train destination ( example in Table 1 ) . Other cases include time-related pairs such as ( taxi arriveBy , taxi leaveAt ) and cross-domain pairs such as ( hotel area , attraction area ) . Our proposed approach considers all possible signals across all domains and slots to generate a dialogue state as a set . Our approach directly optimizes towards the DST evaluation metric Joint Accuracy ( Henderson et al. , 2014b ) , which measures accuracy at state ( set of slots ) level rather than slot level . Our contributions in this work include : ( 1 ) we propose a novel framework of Non-Autoregressive Dialog State Tracking ( NADST ) , which explicitly learns inter-dependencies across slots for decoding dialogue states as a complete set rather than individual slots ; ( 2 ) we propose a non-autoregressive decoding scheme , which not only enjoys low latency for real-time dialogues , but also allows to capture dependencies at token level in addition to slot level ; ( 3 ) we achieve the state-of-the-art performance on the multi-domain task-oriented dialogue dataset “ MultiWOZ 2.1 ” ( Budzianowski et al. , 2018 ; Eric et al. , 2019 ) while significantly reducing the inference latency by an order of magnitude ; ( 4 ) we conduct extensive ablation studies in which our analysis reveals that our models can detect potential signals across slots and dialogue domains to generate more correct “ sets ” of slots for DST . 2 RELATED WORK . Our work is related to two research areas : dialogue state tracking and non-autoregressive decoding . 2.1 DIALOGUE STATE TRACKING . Dialogue State Tracking ( DST ) is an important component in task-oriented dialogues , especially for dialogues with complex domains that require fine-grained tracking of relevant slots . Traditionally , DST is coupled with Natural Language Understanding ( NLU ) . NLU output as tagged user utterances is input to DST models to update the dialogue states turn by turn ( Kurata et al. , 2016 ; Shi et al. , 2016 ; Rastogi et al. , 2017 ) . Recent approaches combine NLU and DST to reduce the credit assignment problem and remove the need for NLU ( Mrkšić et al. , 2017 ; Xu & Hu , 2018 ; Zhong et al. , 2018 ) . Within this body of research , Goel et al . ( 2019 ) differentiates two DST approaches : fixed- and openvocabulary . Fixed-vocabulary approaches are usually retrieval-based methods in which all candidate pairs of ( slot , value ) from a given slot ontology are considered and the models predict a probability score for each pair ( Henderson et al. , 2014c ; Ramadan et al. , 2018 ; Lee et al. , 2019 ) . Recent work has moved towards open-vocabulary approaches that can generate the candidates based on input text i.e . dialogue history ( Lei et al. , 2018 ; Gao et al. , 2019 ; Wu et al. , 2019 ) . Our work is more related to these models , but different from most of the current work , we explicitly consider dependencies among slots and domains to decode dialogue state as a complete set . 2.2 NON-AUTOREGRESSIVE DECODING . Most of prior work in non- or semi-autoregressive decoding methods are used for NMT to address the need for fast translation . Schwenk ( 2012 ) proposes to estimate the translation model probabilities of a phase-based NMT system . Libovickỳ & Helcl ( 2018 ) formulates the decoding process as a sequence labeling task by projecting source sequence into a longer sequence and applying CTC loss ( Graves et al. , 2006 ) to decode the target sequence . Wang et al . ( 2019 ) adds regularization terms to NAT models ( Gu et al. , 2018 ) to reduce translation errors such as repeated tokens and incomplete sentences . Ghazvininejad et al . ( 2019 ) uses a non-autoregressive decoder with masked attention to decode target sequences over multiple generation rounds . A common challenge in nonautoregressive NMT is the large number of sequential latent variables , e.g. , fertility sequences ( Gu et al. , 2018 ) and projected target sequences ( Libovickỳ & Helcl , 2018 ) . These latent variables are used as supporting signals for non- or semi-autoregressive decoding . We reformulate dialogue state as a structured sequence with sub-sequences defined as a concatenation of slot values . This form of dialogue state can be inferred easily from the dialogue state annotation itself whereas such supervision information is not directly available in NMT . The lower semantic complexity of slot values as compared to long sentences in NMT makes it easier to adopt non-autoregressive approaches into DST . According to our review , we are the first to apply a non-autoregressive framework for generation-based DST . Our approach allows joint state tracking across slots , which results in better performance and an order of magnitude lower latency during inference . 3 APPROACH . Our NADST model is composed of three parts : encoders , fertility decoder , and state decoder , as shown in Figure 1 . The input includes the dialogue history X = ( x1 , ... , xN ) and a sequence of applicable ( domain , slot ) pairsXds = ( ( d1 , s1 ) , ... , ( dG , sH ) ) , whereG andH are the total numbers of domains and slots , respectively . The output is the corresponding dialogue states up to the current dialogue history . Conventionally , the output of dialogue state is denoted as tuple ( slot , value ) ( or ( domain-slot , value ) for multi-domain dialogues ) . We reformulate the output as a concatenation of slot values Y di , sj : Y = ( Y d1 , s1 , ... , Y dI , sJ ) = ( yd1 , s11 , y d1 , s1 2 , ... , y dI , sJ 1 , y dI , sJ 2 , ... ) where I and J are the numbers of domains and slots in the output dialogue state , respectively . First , the encoders use token-level embedding and positional encoding to encode the input dialogue history and ( domain , slot ) pairs into continuous representations . The encoded domains and slots are then input to stacked self-attention and feed-forward network to obtain relevant signals across dialogue history and generate a fertility Y dg , shf for each ( domain , slot ) pair ( dg , sh ) . The output of fertility decoder is defined as a sequence : Yfert = Y d1 , s1 f , ... , Y dG , sH f where Y dg , dh f ∈ { 0 , max ( SlotLength ) } . For example , for the MultiWOZ dataset in our experiments , we have max ( SlotLength ) = 9 according to the training data . We follow ( Wu et al. , 2019 ; Gao et al. , 2019 ) to add a slot gating mechanism as an auxiliary prediction . Each gate g is restricted to 3 possible values : “ none ” , “ dontcare ” and “ generate ” . They are used to form higher-level classification signals to support fertility decoding process . The gate output is defined as a sequence : Ygate = Y d1 , s1g , ... , Y dG , sH g . The predicted fertilities are used to form an input sequence to the state decoder for nonautoregressive decoding . The sequence includes sub-sequences of ( dg , sh ) repeated by Y dg , sh f times and concatenated sequentially : Xds×fert = ( ( d1 , s1 ) Y d1 , s1 f , ... , ( dG , sH ) Y dG , sH f ) and ‖Xds×fert‖ = ‖Y ‖ . The decoder projects this sequence through attention layers with dialogue history . During this decoding process , we maintain a memory of hidden states of dialogue history . The output from the state decoder is used as a query to attend on this memory and copy tokens from the dialogue history to generate a dialogue state . Following Lei et al . ( 2018 ) , we incorporate information from previous dialogue turns to predict current turn state by using a partially delexicalized dialogue historyXdel = ( x1 , del , ... , xN , del ) as an input of the model . The dialogue history is delexicalized till the last system utterance by removing real-value tokens that match the previously decoded slot values to tokens expressed as domain-slot . Given a token xn and the current dialogue turn t , the token is delexicalized as follows : xn , del =delex ( xn ) = { domainidx-slotidx , if xn ⊂ Ŷt−1 . xn , otherwise . ( 1 ) domainidx = Xds×fert [ idx ] [ 0 ] , slotidx = Xds×fert [ idx ] [ 1 ] , idx = Index ( xn , Ŷt−1 ) ( 2 ) For example , the user utterance “ I look for a cheap hotel ” is delexicalized to “ I look for a hotel pricerange hotel. ” if the slot hotel pricerange is predicted as “ cheap ” in the previous turn . This approach makes use of the delexicalized form of dialogue history while not relying on an NLU module as we utilize the predicted state from DST model itself . In addition to the belief state , we also use the system action in the previous turn to delexicalize the dialog history in a similar manner , following prior work ( Rastogi et al. , 2017 ; Zhong et al. , 2018 ; Goel et al. , 2019 ) .

Summary: The authors build on recent work for non-autoregressive encoder-decoder models in the context of machine translation (most significantly [Gu, et al., ICLR18]) and adapt this to dialogue state tracking. Specifically, as in [Gu, et al, ICLR18], they use a fertility decoder modified for DST to be on a per-slot basis which is input to a second decoder to generate the (open-vocabulary) tokens representing dialogue state. An interesting aspect of the resulting model as formulated is that the latent space also takes into account interdependencies between generated slot values, which leads to a direct structured prediction-like loss of joint accuracy. Additionally, as slot values have a smaller (and likely peakier) combinatorial space, NAT models actually are more applicable to DST than MT. The resulting model achieves state-of-the-art empirical results on the MultiWOZ dataset while incurring decoding times that are an order of magnitude faster.