T rip P y: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking (2020)

Abstract: Task-oriented dialog systems rely on dialog state tracking (DST) to monitor the user’s goal during the course of an interaction. Multi-domain and open-vocabulary settings complicate the task considerably and demand scalable solutions. In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values. Our model has no need to maintain a list of candidate values. Instead, all values are extracted from the dialog context on-the-fly. A slot is filled by one of three copy mechanisms: (1) Span prediction may extract values directly from the user input; (2) a value may be copied from a system inform memory that keeps track of the system’s inform operations (3) a value may be copied over from a different slot that is already contained in the dialog state to resolve coreferences within and across domains. Our approach combines the advantages of span-based slot filling methods with memory methods to avoid the use of value picklists altogether. We argue that our strategy simplifies the DST task while at the same time achieving state of the art performance on various popular evaluation sets including Multiwoz 2.1, where we achieve a joint goal accuracy beyond 55%.

## introduction the increasing popularity of natural language human-computer interaction urges the development of robust and scalable task-oriented dialog systems. in order to fulfill a user goal, a dialogue system must be capable of extracting meaning and intent from the user input, and be able to keep and update this information over the continuation of the dialog @xcite . this task is called dialog state tracking (dst). because the next dialog system action depends on the current state of the conversation, accurate dialog state tracking (dst) is absolutely vital. u: i'm looking for an expensive restaurant in the center of town. s: there are 33 restaurants [...]. would you like to narrow your search by type of food? u: i do not care about food. surprise me. s: fitzbillies restaurant serves british food, [...]. would you like to hear about any others? [...]. u: that sounds fine. can you book it please and get me the reference number? s: sure , what day and time would you like and how many people ? u: i would like a table for 5 at 11:30 on tuesday [...] s: okay, the booking was successful. [...]. is there anything else i can help you with? u: i'm also looking for a place to stay. it needs [...] free wifi and [be] in the same area as the restaurant. turn domain-slot pair value type coreference 0 restaurant-pricerange expensive span 0 restaurant-area center span 1 restaurant-@xmath0:30 span 3 restaurant-book_day tuesday span 4 hotel-@xmath1. for example, in order to recommend a restaurant to a user, the system needs to know their preferences in terms of price, location, etc. these concepts are encapsulated in an ontology, where dialogue domain (e.g., restaurant or hotel), slot (e.g., price range or location), and value (e.g. cheap or expensive) are defined. solving this information extraction task is prerequisite for forming a belief over the dialog state. traditional approaches to dst operate on a fixed ontology and perform prediction over a pre-defined set of slot-value pairs @xcite @xcite . such approaches perform very well on datasets which are defined over fairly small ontologies. apply these methods to more complex datasets however reveals various limitations @xcite . first, it is often difficult to obtain a complete ontology for a task. second, slot-value pairs that were outside the ontology or the training data are impossible to capture during test time. third, such methods at best scale linearly with the size of the ontology. most importantly, the idea of fixed ontologies is not sustainable, as in real world applications they are subject to constant change. human-computer interactions often need to be defined over multiple domains at the same time, ideally with unrestricted vocabulary. recent approaches to multi-domain and open-vocabulary dst extract values from the dialog context directly by predicting value spans in the input @xcite @xcite . span prediction is a demonstrably potent method to detect relevant information in utterances, but its major drawback is that it only suits extractive values that are explicitly expressed as a sequence of tokens. this is the reason why span-based methods benefit from the support of a picklist, i.e., a list of value candidates from which a system can choose. still, these methods fall short when handling nuanced and subtle phenonema that often occur in natural conversations, such as coreference and value sharing ("i'd like a hotel in the same area as the restaurant."), and implicit choice ("any of those is ok."). in this work, we propose a new approach to value independent multi-domain dst: we call this approach trippy, triple copy strategy dst. 1 our experiments results show that our model is able to handle out-of-vocabulary and rare values very well during test time, demonstrating good generalization. in a detailed analysis we take a closer look at each of the model's components to study their particular roles. ## related work dialog state tracking has been of broad interest to the dialog research community, which is reflected by the existence of a series of dst challenges @xcite . these challenges consistently pushed the boundaries of dst performance. current state-of-the-art has to prove to work on long, diverse conversations in multiple domains with a high slot count and principally unrestricted vocabulary @xcite . dialogs of such complex nature are tough for traditional approaches that rely on the availability of a candidate list due to scalability and generalization issues @xcite @xcite . span-based approaches recently alleviated both problems to some extent. here, slot values are extracted from the input directly by predicting start and end positions in the course of the dialog. for instance, @xcite utilizes an attentionbased recurrent network with a pointer mechanism to extract values from the context. this extractive approach has its limitations, since many expressible values are not found verbatim in the input, but rather mentioned implicitly, or expressed by a variety of rephrasings. with the assistance of contextual models such as bert @xcite , issues arising from expressional variations can be mitigated. recent work has demonstrated that encoding the dialog context with contextual representations supports span prediction to generalize over rephrasings. sumbt @xcite utilizes bert to encode slot ids and candidate values and learns slot-value relationships appearing in dialogs via an attention mechanism. dialog context is encoded with recurrence. bert-dst @xcite employs contextual representations to encode each dialog turn and feeds them into classification heads for value prediction. the dialog history, however, is not considered for slot filling. in @xcite , dst is rendered as a reading comprehension task that is approached with a bert-based dialog context encoder. a slot carryover prediction model determines whether previously detected values should be kept in the ds for the current turn. an alternative to span prediction is value generation. trade @xcite and ma-dst (kumar et al., 2020) generate a ds from the input using a copy mechanism to combine the distributions over a pre-defined vocabulary and the vocabulary of current context. som-dst @xcite applies a similar mechanism for value generation, but takes the previous dialog turn as well as the previous ds as input to bert to predict the current ds. a state operation predictor determines whether a slot actually needs to be updated or not. the downside of generative models is that they tend to produce invalid values, for instance by word repetitions or omissions. recently, a hybrid approach called ds-dst has been proposed that makes use of both span-based and picklist-based prediction for slot-filling @xcite . in contrast to generative approaches, picklist-based and span-based methods use existing word sequences to fill slots. ds-dst somewhat alleviates the limitations of span prediction by filling a subset of slots with a picklist method instead. recent works seemed to reveal a trade-off between the level value independence in a model and the dst performance. @xcite and @xcite solely rely on span-prediction, but their performance lacks behind methods that at least partially rely on a pre-defined list of candidate values. this has impressionably been demonstrated by @xcite . their model could not compete when relying on span-prediction entirely. in contrast, when relying solely on their picklist slot-filling method, they achieved the to-date best performance on multiwoz 2.1. the proposed dualstrategy approach lies favorably between these two extremes. to the best of our knowledge, none of the recent approaches to complex dst tasks such as multi-woz @xcite are value independent in the strict sense. what's more, they tremendously benefit from the use of a value candidate list. our work tackles this limitation by introducing a triple copy strategy that relies on span-prediction as well as memory mechanisms. in contrast to other hybrid approaches such as @xcite , our memory mechanisms create candidate lists of values on-the-fly with the dialog context as only source of information, thus avoiding the use of pre-defined picklists. we let the model decide which strategy to choose for each slot at each turn. our approach differs from @xcite and @xcite in that we consider the dialog history as context in addition to the current turn. we also differ from approaches like @xcite since we do not employ recurrence. like @xcite , we use auxiliary inputs at each turn, but we do so as a late feature fusion strategy. with our slot-value copy mechanism to resolve coreferring value phrases, we employ a method which is reminiscent of @xcite 's slot carryover, but with the sharp distinction that we copy values between different slots, facilitating value sharing within and across domains. ## trippy: triple copy strategy for dst our model expects the following input format to perform dialog state tracking. let @xmath2, m 1 ), . . . , (u t , m t )} be the sequence of turns that comprise a dialog of length t . u t is the user utterance at turn t, m t is the system utterance that preceeds the user utterance. the task of the model is (1) to determine for every turn whether any of the n domain-slot pairs in @xmath3, . . . , s n } is present, (2) to predict the values for each s n and (3) to track the dialog state ds t over the course of the dialog, i.e., for @xmath4, t ]. we employ a triple-copy strategy to fill the slots. the intuition is that values are either explicitly expressed by the user, that they are expressed by the system and referred to by the user via confirmation or rejection, or that they have been expressed earlier in the dialog as assignment to another domain-slot pair (coreference). each of these cases is handled by one of three copy mechanisms. it becomes apparent that slots can not be filled by exclusively resorting to one particular copy method. therefore, we employ slot gates that determine at each turn which method to use to fill the respective slot. figure 2 depicts our model. we encode the dialog context with a bert front-end and feedforward the resulting contextual representations to various classification heads to solve the sub-tasks for dst. the aggregate sequence representation is the input to the slot gates. the sequence of token representations is the input to the span predictors. ## experimental results tables 1, 3 and 2 show the performance of our model in comparison to various baselines. trippy achieves state-of-the-art performance on all four evaluated datasets, with varying distance to the runner-up. most notably, we were able to push the performance on multiwoz 2.1, the most complex task, by another 2.0% absolute compared to the previous top scoring method, achieving 55.3% jga. the improvements on the much smaller datasets woz 2.0, sim-m and sim-r demonstrate ## conclusion we have demonstrated that our approach can handle challenging dst scenarios. having to detect unseen values does not considerably impair our model's general performance. the information extraction capabilities of our proposed model are rooted in the memory-based copy mechanisms and perform well even in extreme cases as discussed in section 5.2. the copy mechanisms are not limited by a predefined vocabulary, since the memories themselves are value agnostic. to further improve the dst capabilities of trippy, we hope to introduce slot independence as at present its tracking abilities are limited to slots that are predefined in the ontology. for that, we would like to expand our approach towards the schema-guided paradigm for dialog modeling. we also would like to employ a more sophisticated update strategy, for example by adding the option to partially forget. there already exists an intriguing set of works focusing on these issues and we hope to incorporate and expand upon it in the future.