Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking (2021)

Abstract: The goal of dialogue state tracking (DST) is to predict the current dialogue state given all previous dialogue contexts. Existing approaches generally predict the dialogue state at every turn from scratch. However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn. Therefore, the mechanism of treating slots equally in each turn not only is inefficient but also may lead to additional errors because of the redundant slot value generation. To address this problem, we devise the two-stage DSS-DST which consists of the Dual Slot Selector based on the current turn dialogue, and the Slot Value Generator based on the dialogue history. The Dual Slot Selector determines each slot whether to update slot value or to inherit the slot value from the previous turn from two aspects: (1) if there is a strong relationship between it and the current turn dialogue utterances; (2) if a slot value with high reliability can be obtained for it through the current turn dialogue. The slots selected to be updated are permitted to enter the Slot Value Generator to update values by a hybrid method, while the other slots directly inherit the values from the previous turn. Empirical results show that our method achieves 56.93%, 60.73%, and 58.04% joint accuracy on MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2 datasets respectively and achieves a new state-of-the-art performance with significant improvements.

## introduction task-oriented dialogue has attracted increasing attention in both the research and industry communities. as a key component in task-oriented dialogue systems, dialogue state tracking (dst) aims to extract user goals or intents and represent them as a compact dialogue state in the form of slot-value pairs of each turn dialogue. dst is an essential part of dialogue management in task-oriented dialogue systems, where the next dialogue system action is selected based on the current dialogue state. early dialogue state tracking approaches extract value for each slot predefined in a single domain @xcite . these methods can be directly adapted to multi-domain conversations by replacing slots in a single domain with domain-slot pairs predefined. in multi-domain dst, some of the previous works study the scalability of the model @xcite , some aim to fully utilizing the dialogue history and context @xcite @xcite , and some attempt to explore the relationship between different slots @xcite . nevertheless, existing approaches generally predict the dialogue state at every turn from scratch. the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn. therefore, the mechanism of treating slots equally in each turn not only is inefficient but also may lead to additional errors because of the redundant slot value generation. to address this problem, we propose a dss-dst which consists of the dual slot selector based on the current turn dialogue, and the slot value generator based on the dialogue history. at each turn, all slots are judged by the dual slot selector first, and only the selected slots are permitted to enter the slot value generator to update their slot value, while the other slots directly inherit the slot value from the previous turn. the dual slot selector is a two-stage judging process. it consists of a preliminary selector and an ultimate selector, which jointly make a judgment for each slot according to the current turn dialogue. the intuition behind this design is that the preliminary selector makes a coarse judgment to exclude most of the irrelevant slots, and then the ultimate selector makes an intensive judgment for the slots selected by the preliminary selector and combines its confidence with the confidence of the preliminary selector to yield the final decision. specifically, the preliminary selector briefly touches on the relationship of current turn dialogue utterances and each slot. then the ultimate selector obtains a temporary slot value for each slot and calculates its reliability. the rationale for the ultimate selector is that if a slot value with high reliability can be obtained through the current turn dialogue, then the slot ought to be updated. eventually, the selected slots enter the slot value generator and a hybrid way of the extractive method and the classification-based method is utilized to generate a value according to the current dialogue utterances and dialogue history. our proposed dss-dst achieves state-of-theart joint accuracy on three of the most actively studied datasets: multiwoz 2.0 @xcite , multiwoz 2.1 @xcite , and multiwoz 2.2 @xcite with joint accuracy of 56.93%, 60.73%, and 58.04%. the results outperform the previous state-of-the-art by +2.54%, +5.43%, and +6.34%, respectively. furthermore, a series of subsequent ablation studies and analysis are conducted to demonstrate the effectiveness of the proposed method. our contributions in this paper are three folds: ## related work traditional statistical dialogue state tracking models combine semantics extracted by spoken language understanding modules to predict the current dialogue state @xcite @xcite or to jointly learn speech understanding @xcite @xcite . with the recent development of deep learning and representation learning, most works about dst focus on encoding dialogue context with deep neural networks and predicting a value for each possible slot @xcite @xcite . for multi-domain dst, slot-value pairs are extended to domain-slot-value pairs for the target @xcite @xcite @xcite @xcite . these models greatly improve the performance of dst, but the mechanism of treating slots equally is inefficient and may lead to additional errors. som-dst @xcite ) considered the dialogue state as an explicit fixed-size memory and proposed a selectively overwriting mechanism. nevertheless, it arguably has limitations because it lacks the explicit exploration of the relationship between slot selection and local dialogue information. on the other hand, dialogue state tracking and machine reading comprehension (mrc) have similarities in many aspects @xcite . in mrc task, unanswerable questions are involved, some studies pay attention to this topic with straightforward solutions. @xcite appended an empty word token to the context and added a simple classification layer to the reader. @xcite used two types of auxiliary loss to predict plausible answers and the answerability of the question. @xcite proposed a retrospective reader that integrates both sketchy and intensive reading. @xcite proposed a verifier layer to context embedding weighted by start and end distribution over the context words representations concatenated to @xcite token representation for bert. the slot selection and the mechanism of local reliability verification in our work are inspired by the answerability prediction in machine reading comprehension. ## the proposed method figure 1 illustrates the architecture of dss-dst. dss-dst consists of embedding, dual slot selector, and slot value generator. in the task-oriented dialogue system, given a dialogue @xmath0, r 1 ); (u 2 , r 2 ) . . . ; (u t , r t )} of t turns where u t represents user utterance and r t represents system response of turn t. we define total_score t j total_score t j ult_score t j ult_score t j pre_score t j pre_score t j embedding preliminary selector dual slot selector the dialogue state at turn t as b @xmath1, v j t )|1 ≤ @xmath2, where s j are the slots, v j t are the corresponding slot values, and j is the total number of such slots. following @xcite , we use the term "slot" to refer to the concatenation of a domain name and a slot name (e.g., "restaurant -f ood").