Controllable User Dialogue Act Augmentation for Dialogue State Tracking (2022)

Abstract: Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialogue acts and control the generation quality, this paper proposes controllable user dialogue act augmentation (CUDA-DST) to augment user utterances with diverse behaviors. With the augmented data, different state trackers gain improvement and show better robustness, achieving the state-of-the-art performance on MultiWOZ 2.1.

## introduction dialogue state tracking (dst) serves as a backbone of task-oriented dialogue systems @xcite , where it aims at keeping track of user intents and associated information in a conversation. the dialogue states encapsulate the required information for the subsequent dialogue components. hence, an accurate dst module is crucial for a dialogue system to perform successful conversations. recently, we have seen tremendous improvement on dst, mainly due to the curation of large datasets @xcite @xcite and many advanced models. they can be broadly categorized into 3 types: span prediction, question answering, and generation-based models. the question answering models define natural language questions for each slot to query the model for the corresponding values @xcite . @xcite proposed trade to perform zeroshot transfer between multiple domains via slotvalue embeddings and a state generator. sim-pletod @xcite combines all components in a task-oriented dialogue system with a pre-trained language model. recently, trippy @xcite categorizes value prediction into 7 types, and designs different prediction strategies for them. this paper focuses on generalized augmentation covering all categories. another research line leverages data augmentation techniques to improve performance @xcite @xcite . most prior work used simple augmentation techniques such as word insertion and state value substitution. with recent advances in pre-trained language models @xcite @xcite , generation-based augmentation has been proposed @xcite . these methods have demonstrated impressive improvement and zero-shot adaptability @xcite , while our work focuses on data augmentation with in-domain data. the closest work is coco @xcite , a framework that generates user utterances given augmented dialogue states. the examples are shown in figure 1 , where the main differences between coco and ours are that 1) coco only augments user utterances in slot and value levels, but dialogue acts and domains are fixed, making augmented data limited. our method can augment reasonable user utterances with diverse dialogue acts and domain switching scenarios. 2) boolean slots and referred slots are not handled by coco due to its higher complexity, while our approach can handle all types of values for better generalization. this paper proposes cuda-dst (controllable user dialogue act augmentation), a generalized framework of generation-based augmentation for improving dst. our contribution is 2-fold: ## controllable user dialogue act augmentation (cuda) the goal of our method is to augment more and diverse user utterances that fit the dialogue context, and then the augmented data can help dst models learn better. more formally, given a system utterance u sys t in the turn t and dialogue history h t-1 before this turn, our approach focuses on augmenting a user dialogue act and state, ât , and generating the corresponding user utterance û usr t . note that each user utterance can be augmented. to achieve this goal, we propose cuda with three components illustrated in figure 2 : 1) a user dialogue act generation process for producing ât , 2) a user utterance generator for producing û usr t , and 3) a state match filtering process. ## experiments to evaluate if our augmented data is beneficial for improving dst models, we perform three popular trackers, trade @xcite , simple-tod (hosseini-asl et al., 2020), and trippy @xcite , on multiwoz 2.1 @xcite . ## conclusion we introduce a generalized data augmentation method for dst by utterance generation with controllable user dialogue act augmentation. experiments show that our approach improves results of multiple state trackers and achieves state-of-theart performance on multiwoz 2.1. further study demonstrates that trackers' robustness and generalization capabilities can be improved by diverse generation covering different user behaviors.