Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation (2020)

Abstract: Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. In this work, we extend this approach to the task of dialog state tracking for goaloriented dialogs. Due to the inherent hierarchical structure of goal-oriented dialogs over utterances and related annotations, the deep generative model must be capable of capturing the coherence among different hierarchies and types of dialog features. We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling the complete aspects of goal-oriented dialogs, including linguistic features and underlying structured annotations, namely speaker information, dialog acts, and goals. The proposed architecture is designed to model each aspect of goal-oriented dialogs using inter-connected latent variables and learns to generate coherent goal-oriented dialogs from the latent spaces. To overcome training issues that arise from training complex variational models, we propose appropriate training strategies. Experiments on various dialog datasets show that our model improves the downstream dialog trackers’ robustness via generative data augmentation. We also discover additional benefits of our unified approach to modeling goal-oriented dialogs – dialog response generation and user simulation, where our model outperforms previous strong baselines.

## introduction data augmentation, a technique that augments the training set with label-preserving synthetic samples, is commonly employed in modern machine learning approaches. it has been used extensively in visual learning pipelines @xcite but less frequently for nlp tasks due to the lack of well-established techniques in the area. while some notable work exists in text classification @xcite , spoken language understanding @xcite , and machine translation @xcite , we still lack the full understanding of utilizing generative models for text augmentation. ideally, a data augmentation technique for supervised tasks must synthesize distribution-preserving and sufficiently realistic samples. current approaches for data augmentation in nlp tasks mostly revolve around thesaurus data augmentation @xcite , in which words that belong to the same semantic role are substituted with one another using a preconstructed lexicon, and noisy data augmentation @xcite where random editing operations create perturbations in the language space. thesaurus data augmentation requires a set of handcrafted semantic dictionaries, which are costly to build and maintain, whereas noisy data augmentation does not synthesize sufficiently realistic samples. the recent trend @xcite @xcite gravitates towards generative data augmentation (gda), a class of techniques that leverage deep generative models such as vaes to delegate the automatic discovery of novel class-preserving samples to machine learning. in this work, we explore gda in the context of dialog modeling and contextual understanding. goal-oriented dialogs occur between a user and a system that communicates verbally to accomplish the user's goals ( for example, a user may verbally refer to a previously mentioned food type as the preferred one -e.g., @xcite ). given the user utterance and historical turns, the state tracker must infer the user's current goals. as such, we can view dialog state tracking as a sparse sequential multi-class classification problem. modeling goal-oriented dialogs for gda requires a novel approach that simultaneously solves state tracking, user simulation @xcite , and utterance generation. various deep models exist for modeling dialogs. the markov approach @xcite employs a sequence-to-sequence variational autoencoder (vae) @xcite structure to predict the next utterance given a deterministic context representation, while the holistic approach @xcite utilizes a set of global latent variables to encode the entire dialog, improving the awareness in general dialog structures. however, current approaches are limited to linguistic features. recently, @xcite proposed a hierarchical vae structure that incorporates the speaker's information, but we have yet to explore a universal approach for encompassing fundamental aspects of goal-oriented dialogs. such a unified model capable of disentangling latents into specific dialog aspects can increase the modeling efficiency and enable interesting extensions based on the finegrained controllability. this paper proposes a novel multi-level hierarchical and recurrent vae structure called variational hierarchical dialog autoencoder (vhda). our model enables modeling all aspects (speaker information, goals, dialog acts, utterances, and general dialog flow) of goal-oriented dialogs in a disentangled manner by assigning latents to each aspect. however, complex and autoregressive vaes are known to suffer from the risk of inference collapse @xcite , in which the model converges to a local optimum where the generator network neglects the latents, reducing the generation controllability. to mitigate the issue, we devise two simple but effective training strategies. our contributions are summarized as follows. the code for reproducing this paper is available at github foot_0 . among the proposed models, neural belief tracker (nbt) @xcite decreases reliance on handcrafted semantic dictionaries by reformulating the classification problem. globallocally self-attentive dialog tracker (glad) @xcite introduces global modules for sharing parameters across slots and local modules, allowing the learning of slot-specific feature representations. globally-conditioned encoder (gce) @xcite improves further by forgoing the separation of global and local modules, allowing the unified module to take slot embeddings for distinction. recently, dialog state trackers based on pre-trained language models have demonstrated their strong performance in many dst tasks @xcite @xcite . while the utilization of large-scale pre-trained language models is not within our scope, we wish to explore further concerning the recent advances in the area. conversation modeling. while the previous approaches for hierarchical dialog modeling relate to the markov assumption @xcite , recent approaches have geared towards utilizing global latent variables for representing the holistic dialog structure @xcite @xcite , which helps in preserving long-term dependencies and total semantics. in this work, we employ global latent variables to maximize the effectiveness in preserving dialog semantics for data augmentation. ## proposed model this section describes vhda, our latent variable model for generating goal-oriented dialog datasets. we first introduce a set of notations for describing core concepts. ## conclusion we proposed a novel hierarchical and recurrent vae-based architecture to capture accurately the semantics of fully annotated goal-oriented dialog 3 the entropy is calculated with respect to the training set distribution 4 the supplementary material includes the full examples.