# Scaling Multi-Domain Dialogue State Tracking via Query Reformulation

arXiv: https://arxiv.org/abs/1903.05164 · HTML: https://mlsanity.com/paper/1903.05164/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

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

- [Slot Self-Attentive Dialogue State Tracking](https://mlsanity.com/paper/2101.09374/) (2021)
- [Semantic Representation for Dialogue Modeling](https://mlsanity.com/paper/2105.10188/) (2021)
- [CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues](https://mlsanity.com/paper/2105.09914/) (2021)
- [Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL](https://mlsanity.com/paper/2106.02282/) (2021)
- [McQueen: a Benchmark for Multimodal Conversational Query Rewrite](https://mlsanity.com/paper/2210.12775/) (2022)
- [User-Initiated Repetition-Based Recovery in Multi-Utterance Dialogue Systems](https://mlsanity.com/paper/2108.01208/) (2021)
- [Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI](https://mlsanity.com/paper/2205.00029/) (2022)
- [Semantic-based Pre-training for Dialogue Understanding](https://mlsanity.com/paper/2209.09146/) (2022)
- [Intelligent Assistant Language Understanding On Device](https://mlsanity.com/paper/2308.03905/) (2023)
- [An empirical assessment of deep learning approaches to task-oriented dialog management](https://mlsanity.com/paper/2108.03478/) (2021)
- [A Survey on Conversational Search and Applications in Biomedicine](https://mlsanity.com/paper/2211.15328/) (2022)
- [Few-Shot Adaptation for Parsing Contextual Utterances with LLMs](https://mlsanity.com/paper/2309.10168/) (2023)
- [On the Robustness of Question Rewriting Systems to Questions of Varying Hardness](https://mlsanity.com/paper/2311.06807/) (2023)
- [Schema Augmentation for Zero-Shot Domain Adaptation in Dialogue State Tracking](https://mlsanity.com/paper/2411.00150/) (2024)
- [Action based Network for Conversation Question Reformulation](https://mlsanity.com/paper/2111.14445/) (2021)
- [ViWOZ: A Multi-Domain Task-Oriented Dialogue Systems Dataset For Low-resource Language](https://mlsanity.com/paper/2203.07742/) (2022)
- [5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair](https://mlsanity.com/paper/2306.01855/) (2023)
- [CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite](https://mlsanity.com/paper/2403.11873/) (2024)
- [Beyond Ontology in Dialogue State Tracking for Goal-Oriented Chatbot](https://mlsanity.com/paper/2410.22767/) (2024)
- [HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST](https://mlsanity.com/paper/2509.19742/) (2025)

## Abstract (arXiv)

We present a novel approach to dialogue state tracking and referring expression resolution tasks. Successful contextual understanding of multi-turn spoken dialogues requires resolving referring expressions across turns and tracking the entities relevant to the conversation across turns. Tracking conversational state is particularly challenging in a multi-domain scenario when there exist multiple spoken language understanding (SLU) sub-systems, and each SLU sub-system operates on its domain-specific meaning representation. While previous approaches have addressed the disparate schema issue by learning candidate transformations of the meaning representation, in this paper, we instead model the reference resolution as a dialogue context-aware user query reformulation task -- the dialog state is serialized to a sequence of natural language tokens representing the conversation. We develop our model for query reformulation using a pointer-generator network and a novel multi-task learning setup. In our experiments, we show a significant improvement in absolute F1 on an internal as well as a, soon to be released, public benchmark respectively.