# Scalable Multi-Domain Dialogue State Tracking

arXiv: https://arxiv.org/abs/1712.10224 · HTML: https://mlsanity.com/paper/1712.10224/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2017.

## Built on by (most cited first, 22 of 22)

- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [Dialogue State Tracking with a Language Model using Schema-Driven Prompting](https://mlsanity.com/paper/2109.07506/) (2021)
- [Slot Self-Attentive Dialogue State Tracking](https://mlsanity.com/paper/2101.09374/) (2021)
- [Pretraining the Noisy Channel Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2103.10518/) (2021)
- [Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation](https://mlsanity.com/paper/2306.04724/) (2023)
- [N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking](https://mlsanity.com/paper/2103.00293/) (2021)
- [Multimodal Dialogue State Tracking](https://mlsanity.com/paper/2206.07898/) (2022)
- [Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?](https://mlsanity.com/paper/2105.03571/) (2021)
- [A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking](https://mlsanity.com/paper/2207.00828/) (2022)
- [Diable: Efficient Dialogue State Tracking as Operations on Tables](https://mlsanity.com/paper/2305.17020/) (2023)
- [DEUX: An Attribute-Guided Framework for Sociable Recommendation Dialog Systems](https://mlsanity.com/paper/2105.00825/) (2021)
- [MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking](https://mlsanity.com/paper/2211.05503/) (2022)
- [Towards Reliable and Empathetic Depression-Diagnosis-Oriented Chats](https://mlsanity.com/paper/2404.05012/) (2024)
- [Oh My Mistake!: Toward Realistic Dialogue State Tracking including Turnback Utterances](https://mlsanity.com/paper/2108.12637/) (2021)
- [A Unified Approach to Entity-Centric Context Tracking in Social Conversations](https://mlsanity.com/paper/2201.12409/) (2022)
- [Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2311.06345/) (2023)
- [Multi-Step Dialogue Workflow Action Prediction](https://mlsanity.com/paper/2311.09593/) (2023)
- [Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2208.02462/) (2022)
- [A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System](https://mlsanity.com/paper/2305.17433/) (2023)
- [Improving Dialogue State Tracking by Joint Slot Modeling](https://mlsanity.com/paper/2109.14144/) (2021)
- [Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings](https://mlsanity.com/paper/2207.00468/) (2022)
- [Candidate Attended Dialogue State Tracking Using BERT](https://mlsanity.com/paper/2607.16021/) (2026)

## Abstract (arXiv)

Dialogue state tracking (DST) is a key component of task-oriented dialogue systems. DST estimates the user's goal at each user turn given the interaction until then. State of the art approaches for state tracking rely on deep learning methods, and represent dialogue state as a distribution over all possible slot values for each slot present in the ontology. Such a representation is not scalable when the set of possible values are unbounded (e.g., date, time or location) or dynamic (e.g., movies or usernames). Furthermore, training of such models requires labeled data, where each user turn is annotated with the dialogue state, which makes building models for new domains challenging. In this paper, we present a scalable multi-domain deep learning based approach for DST. We introduce a novel framework for state tracking which is independent of the slot value set, and represent the dialogue state as a distribution over a set of values of interest (candidate set) derived from the dialogue history or knowledge. Restricting these candidate sets to be bounded in size addresses the problem of slot-scalability. Furthermore, by leveraging the slot-independent architecture and transfer learning, we show that our proposed approach facilitates quick adaptation to new domains.