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

arXiv: https://arxiv.org/abs/2008.12335 · HTML: https://mlsanity.com/paper/2008.12335/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems](https://mlsanity.com/paper/2110.06800/) (2021)
- [Amendable Generation for Dialogue State Tracking](https://mlsanity.com/paper/2110.15659/) (2021)
- [Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2204.06677/) (2022)
- [SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services](https://mlsanity.com/paper/2105.08049/) (2021)
- [Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema](https://mlsanity.com/paper/2303.16252/) (2023)
- [OLViT: Multi-Modal State Tracking via Attention-Based Embeddings for Video-Grounded Dialog](https://mlsanity.com/paper/2402.13146/) (2024)
- [The Convergence of Schema-Guided Dialogue Systems and the Model Context Protocol](https://mlsanity.com/paper/2602.18764/) (2026)
- [Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach](https://mlsanity.com/paper/2603.24747/) (2026)

## Abstract (arXiv)

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.