# SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services

arXiv: https://arxiv.org/abs/2105.08049 · HTML: https://mlsanity.com/paper/2105.08049/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 16 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [Building a Conversational Agent Overnight with Dialogue Self-Play](https://mlsanity.com/paper/1801.04871/) (2018)
- [An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification](https://mlsanity.com/paper/1912.09297/) (2019)
- [Fine-Tuning BERT for Schema-Guided Zero-Shot Dialogue State Tracking](https://mlsanity.com/paper/2002.00181/) (2020)
- [A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/2008.12335/) (2020)
- [Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems](https://mlsanity.com/paper/2001.07526/) (2020)
- [The SPPD System for Schema Guided Dialogue State Tracking Challenge](https://mlsanity.com/paper/2006.09035/) (2020)

## Built on by (most cited first, 4 of 4)

- [SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems](https://mlsanity.com/paper/2110.06800/) (2021)
- [Show, Don't Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue](https://mlsanity.com/paper/2204.04327/) (2022)
- [Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking](https://mlsanity.com/paper/2306.09340/) (2023)
- [Graph Integrated Language Transformers for Next Action Prediction in Complex Phone Calls](https://mlsanity.com/paper/2404.08155/) (2024)

## Abstract (arXiv)

Dialogue state tracking is an essential part of goal-oriented dialogue systems, while most of these state tracking models often fail to handle unseen services. In this paper, we propose SGD-QA, a simple and extensible model for schema-guided dialogue state tracking based on a question answering approach. The proposed multi-pass model shares a single encoder between the domain information and dialogue utterance. The domain's description represents the query and the dialogue utterance serves as the context. The model improves performance on unseen services by at least 1.6x compared to single-pass baseline models on the SGD dataset. SGD-QA shows competitive performance compared to state-of-the-art multi-pass models while being significantly more efficient in terms of memory consumption and training performance. We provide a thorough discussion on the model with ablation study and error analysis.