Recent Neural Methods on Dialogue State Tracking for Task-Oriented Dialogue Systems: A Survey (2021)

Abstract: This paper aims at providing a comprehensive overview of recent developments in dialogue state tracking (DST) for task-oriented conversational systems. We introduce the task, the main datasets that have been exploited as well as their evaluation metrics, and we analyze several proposed approaches. We distinguish between static ontology DST models, which predict a fixed set of dialogue states, and dynamic ontology models, which can predict dialogue states even when the ontology changes. We also discuss the model’s ability to track either single or multiple domains and to scale to new domains, both in terms of knowledge transfer and zero-shot learning. We cover a period from 2013 to 2020, showing a significant increase of multiple domain methods, most of them utilizing pre-trained language models.

## introduction task-oriented dialogue systems enable users to accomplish tasks, such as ticket booking, restaurant reservation, and customer support, by interacting in natural language. the ability to accurately track the user's requirements during the dialogue is crucial to enable a consistent and effective dialogue @xcite . dialogue systems track such information using a dialogue state tracker (dst) component, where a dialogue state is represented with slot-value pairs, each denoting a specific user's requirement. the accurate tracking of this information is crucial, as downstream components, like the dialog manager, rely on the dialogue state to choose the next action of the system. in recent years the performance of several natural language processing (nlp) tasks, including dialogue state tracking @xcite , has been pushed forward by neural networkbased approaches. the dst task actually merges some aspects of natural language understanding in dialogues, although it is more complex than the standard slot filling task. in fact, while slot filling involves predicting the slot-value pairs referred in a particular turn in dialogue @xcite , dst involves predicting the slot-value pairs at the dialogue level until the current turn. the complexity of dst has driven research to propose various neural approaches, including recurrent neural networks-based @xcite @xcite @xcite , attention-based models @xcite @xcite , and the very recent transformer-based models @xcite @xcite @xcite @xcite . in addition, the rapid progress of nlp has provided technologies to address several dst challenges, including predicting slot-values that are not present in training data, moving from rule-based to learning methods for dialogue state updating, and addressing long-term dependency, a crucial aspect in dialogue. furthermore, encouraged by the considerable success in modeling single domain dialogues @xcite @xcite , research on dst has recently moved toward building models that can handle multiple domains @xcite @xcite , and that are flexible enough to be adapted to new domains @xcite @xcite . although such rapid signs of progress have generated an impressive amount of research in dst, including several datasets and experimental material, to the best of our knowledge, such a massive amount of recent work has been only poorly documented @xcite , and there is not an updated survey of the field. this paper intends to fill such a gap, providing ## dialogue state tracking we first introduce the notion of dialogue state, and then describe the dst task, giving details on different dialogue state prediction strategies. ## dst datasets and evaluation metrics in this section we introduce the datasets that have been used in dst in a period from 2013 to 2020, as well as the evaluation metrics for the task. ## static ontology dst models the main distinguishing characteristic of dst models, in our opinion, is their capacity to predict dialogue states either from a fixed set of slot-values (i.e., from a static ontology) or from a possible open set of slot-values (i.e., from a dynamic ontology). static ontology models rely on a fixed ontology to predict the dialogue state. this means that the set of slot-values is predefined, and that a model can only predict for those predefined values. these models typically consist of an input layer that transforms each input token into an embedding, of an encoder layer that encodes the input to a hidden state h t , and of an output layer that predicts the slot value based on h t . considering that the set of possible slot-values is predefined, there are two approaches used for the output layer: i) a feed-forward layer, which receives the input representation and produces scores equal to the # of slot-values; ii) an output layer that receives both the input and the slot-value representations and compares them with each of the slot-value representations providing a score for each slot-value. the obtained score can then be normalized using a non-linear activation function, either softmax, to get a probability distribution over all the slot-value pairs, or sigmoid, to get the individual probability for each slot-value pair. figure 2 shows the standard architecture of the two approaches. we now review few challenges that have been addressed in static ontology models, including delexicalization, data-driven dst, parameter sharing, latency in prediction, and the use of pre-trained language models. performances of the systems are all reported in delexicalization. delexicalization is an effective approach adopted to counter imbalanced training data for slot-values. in this regard, the slot values in the input are replaced with labels corresponding to slot names. for instance, i want chinese food is delexicalised as i want f.value f.slot. it has to be noted that replacing slot-values needs a semantic dictionary listing the possible values for each slot. @xcite has proposed a word-based dst with recurrent neural networks that uses delexicalization on top of an input representation based on automatic speech recognition. this allows to improve the system robustness with respect to the user expressions mentioning slot values. ## dynamic ontology dst models the models discussed in section 4 rely on a fixed slot-value set, which is assumed to be available before making the prediction. this is a severe limitation to domains where compiling the slot-value set . is costly, or the set of possible slot-values is open (e.g., departure time, restaurant name, etc.). for this reason, various studies have focused on developing models that can track slot values even if they are not defined in the ontology. two major approaches for dynamic ontology models are: i) copy the slot value from the user input to the output; and ii) generate the slot value as the output. figure 3 presents the schema of a model using the combination of both approaches. one significant difference between static ontology and dynamic ontology models is that while the output vocabulary in the static ontology is limited (i.e., equal to # of slot-values), in a dynamic ontology setting the output vocabulary is much larger. copy and pointer networks. copy mechanism @xcite and pointer networks @xcite are the main approaches in neural networks to make predictions on the input tokens. they both rely on the attention mechanism @xcite to obtain scores over the input tokens. @xcite proposed an end-to-end dst architecture based on pointer networks, showing efficient tracking of unseen slot values in a datadriven approach on the dstc2 dataset. however, since pointer networks can only make predictions on the input tokens, they cannot be directly applied for all slots and require postprocessing of predicted values. @xcite proposed a transferable multi-domain state generator trade, the first generation-based dst that incorporates the copy mechanism with a slot-gate. figure 3 shows the architecture of the trade model. trade is based on an encoder-decoder architecture consisting of a three-way classifier that predicts over probabilities ptr, none, and dontcare. if the value is not expressed, it is predicted as none, if no constraint then dontcare and, if the value is expressed in the input, then ptr is predicted by the slot-gate. on ptr prediction, the corresponding value needs to be decoded by the decoder layer (referred as state generator). the state generator layer is initialized with both the domain and the slot representation, and generates the dialogue state using a recurrent architecture. as all the parameters are shared for all slots and domains, trade enables the transfer of knowledge from one domain to another, which has opened research directions in zero-shot approaches for dst with promising results. categorical and non-categorical slot-values. dst models based on dynamic ontology are supposed to address predictions particularly for noncategorical slots, which admit an open set of values. in this direction (zhang et al., 2019) proposed a dual-strategy approach that can predict both over a predefined set of slot-values and can generate values based on the input dialogue. if a given slot is labeled as categorical (i.e., possible values for the slot are predefined), the output layer predicts a score over the possible slot-values, while, if the slot is labeled as non-categorical, the span (i.e., start and end positions) of the value is decoded from the input tokens. (heck et al., 2020) proposed a triple copy strategy (trippy) for dst. the slot-values function-based update. the approaches reported so far for dynamic ontology either use a rule-based update mechanism or they predict the complete dialogue state at each turn from scratch. a function-based update mechanism is proposed in som-dst, selectively overwriting memory model @xcite , that tracks the dialogue state in memory and predicts only the dialogue state update. first, one of the four slot operations (i.e., {carryover, delete, dontcare, up-date}) is initially predicted to decide the decoding strategy for the slot. carryover denotes that the slot-value from the previous dialogue state is carried over, delete denotes that the user retracts the slot-value and update denotes that a new slot-value needs to be predicted and updated to the dialogue state. then, based on the state update prediction, a dialogue state is decoded. schema-guided models. so far, all of dst approaches focus on modeling a given ontology, without considering the portability and flexibility of the model to accommodate other datasets or domains. though some models, such as trade, som-dst, ds-picklist and trippy @xcite @xcite can make predictions for a new domain, they are typically modeled only for the domains in a specific dataset, and the flexibility of the model to incorporate new domains is not an inherent feature. this is basically due to the different ontology schema used in each dataset, which make them incompatible. in this context, the schema-guided dataset (sgd) (see section 3.4), puts forth a standard schema to be adopted for all domains. in sgd, a standard schema structure is adopted, slots are classified as either categorical or non-categorical, and each slot includes a brief natural language description. then, a new dataset needs to follow this schema, which would enable the model to predict dialogue states without any change in the architecture. several works exploit the potential of the sgd dataset. @xcite proposed a domain aware dst da-dst based on @xcite to effectively predict slot-values specific to each domain. da-dst uses multiple multi-head attention to extract both a domain-and a slot-specific representation from the input, and then combines them to predict the dialogue state. @xcite use a graph attention network exploiting the slot relations to learn the representation of the ontology schema and the input simultaneously. @xcite propose a neural reading comprehension approach to dst. here, for each slot i a question (q i : what is the value for slot i?) is formulated and treat the dialogue d t as a passage. finally, @xcite propose the first non-auto-regressive dst approach (nadst) to learn the inter-dependencies across slots. this approach allows for a parallel decoding strategy to considerably reduce the latency of the models incomparison with recurrent architectures. ## take-away points this section presents take-away points intended to underline both limitations and improvements in different scenarios. appendix a provides additional details of the models discussed in this survey. ## dst challenges and future directions the addition of new slots and new domains is inevitable in real-world conversational applications when a dialogue system is deployed @xcite , ## conclusion we have surveyed a number of recent studies addressing neural-network-based dst and have discussed both the task and the major datasets available to the research community. we grouped models according to their capacity to make dialogue state predictions either with respect to a static ontology (i.e., a fixed set of dialogue states) or with respect to a dynamic ontology (i.e., an open set of dialogue states). we also reported about dst models' progress towards modeling trackers that perform few-shot and zero-shot learning to accommodate new domains, this way opening multiple opportunities both in research and industry.