Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition (2021)

Abstract: We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for clas- sification and sequence labelling) to jointly extract dialogue states. Experimental results based on the MultiWoz 2.0 dataset validates the superiority of our proposed model in terms of both complexity and scalability when compared to the state-of-the-art methods, especially in the scenario of multi-domain dialogues entangled with many turns of utterances.

## introduction dialogue state tracking (dst), responsible for extracting user goals/intentions from dialogues, is a core component in task-oriented dialogue systems @xcite . a dialogue state is commonly represented as a (domain, slot type, slot value) triplet, e.g., (hotel, people, 3). we show an illustrated example of a multi-domain dialogue in figure 1 , which involves two domains, i.e., train and hotel. previous approaches for dst usually fall into the following four categories: (1) adopt encoderdecoder models to generates states @xcite @xcite @xcite ; (2) cast dst as a multilabel classification task when a full candidate-value list is available @xcite @xcite ; (3) employ span-based methods to directly extract the states @xcite ; and (4) combine both classification-based and spanbased methods to jointly complete the dialogue state extraction @xcite . the most related work to ours is ds-dst @xcite , a joint model which highlights the problem that using classification-based or span-figure 1 : a multi-domain dialogue example extracted from multiwoz 2.0. the s-type slot values are marked in bold and the arrow points to a pair of c-type slots and its corresponding value. the domain discussed changes from "train" to "hotel" at the fourth turn. refer to section 2 for the definitions of c-type and s-type. to tackle these issues, we propose a fast and scalable method called emd, where we decompose dst into three classification modules and one sequence labeling module to jointly extract the dialogue states. the benefits of our approach are summarised below: ## our proposed model figure 1 shows a typical multi-domain dialogue example, from which we can observe that some slot values can be directly found from utterances (e.g. cambridge and london), while other slot values are implicit which are more challenging to discover, e.g., requiring classification to infer the values (e.g. internet:yes). we divide slots into two categories that are handled by two two separate modules: s-type slots whose values could be extracted from dialogue utterances, and c-type slots whose values do not appear in utterances and are chosen from one of the three values {yes, no, don't care}. ## conclusion in this paper, we propose to decompose dst into multiple submodules to jointly estimate dialogue states. experimental results based on the multi-woz 2.0 dataset show that our model not only reduces the model complexity, but also gives high scalability in coping with multi-domain and long task-oriented dialogue scenarios.