Papers › A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset

A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset

27 Aug 2020arXiv:2008.12335archive 2025-07-28

Vahid Noroozi, Yang Zhang, Evelina Bakhturina, Tomasz Kornuta

Dialog State Tracking (DST) is one of the most crucial modules for goal-oriented dialogue systems. In this paper, we introduce FastSGT (Fast Schema Guided Tracker), a fast and robust BERT-based model for state tracking in goal-oriented dialogue systems. The proposed model is designed for the Schema-Guided Dialogue (SGD) dataset which contains natural language descriptions for all the entities including user intents, services, and slots. The model incorporates two carry-over procedures for handling the extraction of the values not explicitly mentioned in the current user utterance. It also uses multi-head attention projections in some of the decoders to have a better modelling of the encoder outputs. In the conducted experiments we compared FastSGT to the baseline model for the SGD dataset. Our model keeps the efficiency in terms of computational and memory consumption while improving the accuracy significantly. Additionally, we present ablation studies measuring the impact of different parts of the model on its performance. We also show the effectiveness of data augmentation for improving the accuracy without increasing the amount of computational resources.

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Data AugmentationDialogue State TrackingGoal-Oriented Dialogue Systems

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Introduced by this paper: FastSGT

AdamAttentionAttention DropoutBERTDense ConnectionsDropoutFastSGTLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionSGDSoftmaxWeight DecayWordPiece

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