# Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing

arXiv: https://arxiv.org/abs/1807.06517 · HTML: https://mlsanity.com/paper/1807.06517/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 29 of 29)

- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [DeepStruct: Pretraining of Language Models for Structure Prediction](https://mlsanity.com/paper/2205.10475/) (2022)
- [Zero-shot Generalization in Dialog State Tracking through Generative Question Answering](https://mlsanity.com/paper/2101.08333/) (2021)
- [Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog](https://mlsanity.com/paper/2205.10400/) (2022)
- [Navigating Rifts in Human-LLM Grounding: Study and Benchmark](https://mlsanity.com/paper/2503.13975/) (2025)
- [HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data](https://mlsanity.com/paper/2204.13243/) (2022)
- [DS-TOD: Efficient Domain Specialization for Task Oriented Dialog](https://mlsanity.com/paper/2110.08395/) (2021)
- [Amendable Generation for Dialogue State Tracking](https://mlsanity.com/paper/2110.15659/) (2021)
- [Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2204.06677/) (2022)
- [X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents](https://mlsanity.com/paper/2306.17674/) (2023)
- [Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations](https://mlsanity.com/paper/2106.12384/) (2021)
- [Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance](https://mlsanity.com/paper/2109.04349/) (2021)
- [Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks](https://mlsanity.com/paper/2310.09436/) (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)
- [Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking](https://mlsanity.com/paper/2107.12578/) (2021)
- [SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain](https://mlsanity.com/paper/2106.01972/) (2021)
- [Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation](https://mlsanity.com/paper/2302.09424/) (2023)
- [Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema](https://mlsanity.com/paper/2303.16252/) (2023)
- [An Empirical Study of Cross-Lingual Transferability in Generative Dialogue State Tracker](https://mlsanity.com/paper/2101.11360/) (2021)
- [Prompt-based Generative Approach towards Multi-Hierarchical Medical Dialogue State Tracking](https://mlsanity.com/paper/2203.09946/) (2022)
- [XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking](https://mlsanity.com/paper/2204.05895/) (2022)
- [ShopTalk: A System for Conversational Faceted Search](https://mlsanity.com/paper/2109.00702/) (2021)
- [TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization](https://mlsanity.com/paper/2204.04504/) (2022)
- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)
- [Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking](https://mlsanity.com/paper/2302.08220/) (2023)
- [Learn to Focus: Hierarchical Dynamic Copy Network for Dialogue State Tracking](https://mlsanity.com/paper/2107.11778/) (2021)
- [Improving Dialogue State Tracking by Joint Slot Modeling](https://mlsanity.com/paper/2109.14144/) (2021)
- [Closing the Expression Gap in LLM Instructions via Socratic Questioning](https://mlsanity.com/paper/2510.27410/) (2025)

## Abstract (arXiv)

Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems. The tasks that dialogue systems are trying to solve are becoming increasingly complex, requiring scalability to multi domain, semantically rich dialogues. However, most current approaches have difficulty scaling up with domains because of the dependency of the model parameters on the dialogue ontology. In this paper, a novel approach is introduced that fully utilizes semantic similarity between dialogue utterances and the ontology terms, allowing the information to be shared across domains. The evaluation is performed on a recently collected multi-domain dialogues dataset, one order of magnitude larger than currently available corpora. Our model demonstrates great capability in handling multi-domain dialogues, simultaneously outperforming existing state-of-the-art models in single-domain dialogue tracking tasks.