  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.
