# An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification

arXiv: https://arxiv.org/abs/1912.09297 · HTML: https://mlsanity.com/paper/1912.09297/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

## Built on by (most cited first, 20 of 20)

- [Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.00783/) (2021)
- [Description-Driven Task-Oriented Dialog Modeling](https://mlsanity.com/paper/2201.08904/) (2022)
- [SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems](https://mlsanity.com/paper/2110.06800/) (2021)
- [Slot Self-Attentive Dialogue State Tracking](https://mlsanity.com/paper/2101.09374/) (2021)
- [Towards LLM-driven Dialogue State Tracking](https://mlsanity.com/paper/2310.14970/) (2023)
- [Generalized Zero-shot Intent Detection via Commonsense Knowledge](https://mlsanity.com/paper/2102.02925/) (2021)
- [Exploring Zero and Few-shot Techniques for Intent Classification](https://mlsanity.com/paper/2305.07157/) (2023)
- [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)
- [Linguistically-Enriched and Context-Aware Zero-shot Slot Filling](https://mlsanity.com/paper/2101.06514/) (2021)
- [A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking](https://mlsanity.com/paper/2207.00828/) (2022)
- [Zero-Shot Cross-Domain Dialogue State Tracking via Dual Low-Rank Adaptation](https://mlsanity.com/paper/2407.21633/) (2024)
- [SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services](https://mlsanity.com/paper/2105.08049/) (2021)
- [A Dialogue-based Information Extraction System for Medical Insurance Assessment](https://mlsanity.com/paper/2107.05866/) (2021)
- [More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking](https://mlsanity.com/paper/2303.09905/) (2023)
- [Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking](https://mlsanity.com/paper/2306.09340/) (2023)
- [A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding](https://mlsanity.com/paper/2409.15861/) (2024)
- [Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead](https://mlsanity.com/paper/2110.12412/) (2021)
- [Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features](https://mlsanity.com/paper/2309.11307/) (2023)
- [Schema Augmentation for Zero-Shot Domain Adaptation in Dialogue State Tracking](https://mlsanity.com/paper/2411.00150/) (2024)

## Abstract (arXiv)

This paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue State Tracking. The goal of this task is to predict the intents and slots in each user turn to complete the dialogue state tracking (DST) based on the information provided by the task's schema. Different from traditional stage-wise DST, we propose an end-to-end DST system to avoid error accumulation between the dialogue turns. The DST system consists of a machine reading comprehension (MRC) model for non-categorical slots and a Wide & Deep model for categorical slots. As far as we know, this is the first time that MRC and Wide & Deep model are applied to DST problem in a fully end-to-end way. Experimental results show that our framework achieves an excellent performance on the test dataset including 50% zero-shot services with a joint goal accuracy of 0.8652 and a slot tagging F1-Score of 0.9835.