# Schema-Guided Dialogue State Tracking Task at DSTC8

arXiv: https://arxiv.org/abs/2002.01359 · HTML: https://mlsanity.com/paper/2002.01359/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2020.

## Built on by (most cited first, 30 of 32)

- [Dialogue State Tracking with a Language Model using Schema-Driven Prompting](https://mlsanity.com/paper/2109.07506/) (2021)
- [Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.00783/) (2021)
- [InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning](https://mlsanity.com/paper/2205.12673/) (2022)
- [ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?](https://mlsanity.com/paper/2306.01386/) (2023)
- [SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems](https://mlsanity.com/paper/2110.06800/) (2021)
- ["Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking](https://mlsanity.com/paper/2207.14627/) (2022)
- [AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation](https://mlsanity.com/paper/2106.05589/) (2021)
- [Learning to Select External Knowledge with Multi-Scale Negative Sampling](https://mlsanity.com/paper/2102.02096/) (2021)
- [Show, Don't Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue](https://mlsanity.com/paper/2204.04327/) (2022)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [Schema-Guided Paradigm for Zero-Shot Dialog](https://mlsanity.com/paper/2106.07056/) (2021)
- [GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling](https://mlsanity.com/paper/2106.07055/) (2021)
- [A Differentiable Language Model Adversarial Attack on Text Classifiers](https://mlsanity.com/paper/2107.11275/) (2021)
- [UniDU: Towards A Unified Generative Dialogue Understanding Framework](https://mlsanity.com/paper/2204.04637/) (2022)
- [Arch-Router: Aligning LLM Routing with Human Preferences](https://mlsanity.com/paper/2506.16655/) (2025)
- [Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestration](https://mlsanity.com/paper/2212.06564/) (2022)
- [Instilling Type Knowledge in Language Models via Multi-Task QA](https://mlsanity.com/paper/2204.13796/) (2022)
- [A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking](https://mlsanity.com/paper/2207.00828/) (2022)
- [Attention Guided Dialogue State Tracking with Sparse Supervision](https://mlsanity.com/paper/2101.11958/) (2021)
- [Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking](https://mlsanity.com/paper/2203.03123/) (2022)
- [Diable: Efficient Dialogue State Tracking as Operations on Tables](https://mlsanity.com/paper/2305.17020/) (2023)
- [Toward More Accurate and Generalizable Evaluation Metrics for Task-Oriented Dialogs](https://mlsanity.com/paper/2306.03984/) (2023)
- [TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration](https://mlsanity.com/paper/2601.04544/) (2026)
- [Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System](https://mlsanity.com/paper/2308.08169/) (2023)
- [GKS: Graph-based Knowledge Selector for Task-oriented Dialog System](https://mlsanity.com/paper/2112.03719/) (2021)
- [Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction](https://mlsanity.com/paper/2303.05034/) (2023)
- [HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot Filling](https://mlsanity.com/paper/2310.09135/) (2023)
- [PyTOD: Programmable Task-Oriented Dialogue with Execution Feedback](https://mlsanity.com/paper/2508.15456/) (2025)
- [Towards Handling Unconstrained User Preferences in Dialogue](https://mlsanity.com/paper/2109.08650/) (2021)
- [DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing](https://mlsanity.com/paper/2303.09827/) (2023)

## Abstract (arXiv)

This paper gives an overview of the Schema-Guided Dialogue State Tracking task of the 8th Dialogue System Technology Challenge. The goal of this task is to develop dialogue state tracking models suitable for large-scale virtual assistants, with a focus on data-efficient joint modeling across domains and zero-shot generalization to new APIs. This task provided a new dataset consisting of over 16000 dialogues in the training set spanning 16 domains to highlight these challenges, and a baseline model capable of zero-shot generalization to new APIs. Twenty-five teams participated, developing a range of neural network models, exceeding the performance of the baseline model by a very high margin. The submissions incorporated a variety of pre-trained encoders and data augmentation techniques. This paper describes the task definition, dataset and evaluation methodology. We also summarize the approach and results of the submitted systems to highlight the overall trends in the state-of-the-art.