# Towards Universal Dialogue State Tracking

arXiv: https://arxiv.org/abs/1810.09587 · HTML: https://mlsanity.com/paper/1810.09587/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 30 of 33)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI](https://mlsanity.com/paper/2205.11029/) (2022)
- [Conversational Question Answering: A Survey](https://mlsanity.com/paper/2106.00874/) (2021)
- [HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data](https://mlsanity.com/paper/2204.13243/) (2022)
- [Slot Self-Attentive Dialogue State Tracking](https://mlsanity.com/paper/2101.09374/) (2021)
- [Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation](https://mlsanity.com/paper/2405.13037/) (2024)
- [Adaptive Retrieval-Augmented Generation for Conversational Systems](https://mlsanity.com/paper/2407.21712/) (2024)
- [Amendable Generation for Dialogue State Tracking](https://mlsanity.com/paper/2110.15659/) (2021)
- [Effective Sequence-to-Sequence Dialogue State Tracking](https://mlsanity.com/paper/2108.13990/) (2021)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models](https://mlsanity.com/paper/2311.05720/) (2023)
- [Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking](https://mlsanity.com/paper/2109.13620/) (2021)
- [Smoothing Dialogue States for Open Conversational Machine Reading](https://mlsanity.com/paper/2108.12599/) (2021)
- [LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking](https://mlsanity.com/paper/2205.02550/) (2022)
- [Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking](https://mlsanity.com/paper/2107.12578/) (2021)
- [A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking](https://mlsanity.com/paper/2207.00828/) (2022)
- [Knowledge-grounded Dialog State Tracking](https://mlsanity.com/paper/2210.06656/) (2022)
- [OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue](https://mlsanity.com/paper/2209.04595/) (2022)
- [MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking](https://mlsanity.com/paper/2211.05503/) (2022)
- [Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema](https://mlsanity.com/paper/2303.16252/) (2023)
- [Towards a Neural Era in Dialogue Management for Collaboration: A Literature Survey](https://mlsanity.com/paper/2307.09021/) (2023)
- [Multi-Step Dialogue Workflow Action Prediction](https://mlsanity.com/paper/2311.09593/) (2023)
- [Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Models](https://mlsanity.com/paper/2501.00953/) (2025)
- [ShopTalk: A System for Conversational Faceted Search](https://mlsanity.com/paper/2109.00702/) (2021)
- [Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking](https://mlsanity.com/paper/2302.08220/) (2023)
- [UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking](https://mlsanity.com/paper/2310.10492/) (2023)
- [Efficient Dialogue State Tracking by Masked Hierarchical Transformer](https://mlsanity.com/paper/2106.14433/) (2021)
- [ViWOZ: A Multi-Domain Task-Oriented Dialogue Systems Dataset For Low-resource Language](https://mlsanity.com/paper/2203.07742/) (2022)
- [Schema Encoding for Transferable Dialogue State Tracking](https://mlsanity.com/paper/2210.02351/) (2022)

## Abstract (arXiv)

Dialogue state tracking is the core part of a spoken dialogue system. It estimates the beliefs of possible user's goals at every dialogue turn. However, for most current approaches, it's difficult to scale to large dialogue domains. They have one or more of following limitations: (a) Some models don't work in the situation where slot values in ontology changes dynamically; (b) The number of model parameters is proportional to the number of slots; (c) Some models extract features based on hand-crafted lexicons. To tackle these challenges, we propose StateNet, a universal dialogue state tracker. It is independent of the number of values, shares parameters across all slots, and uses pre-trained word vectors instead of explicit semantic dictionaries. Our experiments on two datasets show that our approach not only overcomes the limitations, but also significantly outperforms the performance of state-of-the-art approaches.