# Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking

arXiv: https://arxiv.org/abs/2204.06677 · HTML: https://mlsanity.com/paper/2204.06677/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 35 references that are in the map.
- First posted on arXiv 14 Apr 2022.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines](https://mlsanity.com/paper/2007.12720/) (2020)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://mlsanity.com/paper/2005.02877/) (2020)
- [MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2009.12005/) (2020)
- [Recent Advances and Challenges in Task-oriented Dialog System](https://mlsanity.com/paper/2003.07490/) (2020)
- [Efficient Dialogue State Tracking by Selectively Overwriting Memory](https://mlsanity.com/paper/1911.03906/) (2019)
- [Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking](https://mlsanity.com/paper/1910.03544/) (2019)
- [A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning](https://mlsanity.com/paper/2009.08115/) (2020)
- [Global-Locally Self-Attentive Dialogue State Tracker](https://mlsanity.com/paper/1805.09655/) (2018)
- [A Sequence-to-Sequence Approach to Dialogue State Tracking](https://mlsanity.com/paper/2011.09553/) (2020)
- [From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap](https://mlsanity.com/paper/2004.05827/) (2020)
- [Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering](https://mlsanity.com/paper/1911.06192/) (2019)
- [Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing](https://mlsanity.com/paper/1807.06517/) (2018)
- [Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2004.03386/) (2020)
- [Slot Self-Attentive Dialogue State Tracking](https://mlsanity.com/paper/2101.09374/) (2021)
- [Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation](https://mlsanity.com/paper/1909.00754/) (2019)
- [Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking](https://mlsanity.com/paper/2104.04466/) (2021)
- [A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/2008.12335/) (2020)
- [Multi-Domain Dialogue State Tracking based on State Graph](https://mlsanity.com/paper/2010.11137/) (2020)

## Built on by (most cited first, 18 of 18)

- [Learning to Execute Actions or Ask Clarification Questions](https://mlsanity.com/paper/2204.08373/) (2022)
- ["Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking](https://mlsanity.com/paper/2207.14627/) (2022)
- [BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency](https://mlsanity.com/paper/2208.08320/) (2022)
- [Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering](https://mlsanity.com/paper/2308.15231/) (2023)
- [GraphLearner: Graph Node Clustering with Fully Learnable Augmentation](https://mlsanity.com/paper/2212.03559/) (2022)
- [Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking](https://mlsanity.com/paper/2306.00434/) (2023)
- [Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues](https://mlsanity.com/paper/2305.16798/) (2023)
- [Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation](https://mlsanity.com/paper/2306.04724/) (2023)
- [Are LLMs Robust for Spoken Dialogues?](https://mlsanity.com/paper/2401.02297/) (2024)
- [MetaASSIST: Robust Dialogue State Tracking with Meta Learning](https://mlsanity.com/paper/2210.12397/) (2022)
- [MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking](https://mlsanity.com/paper/2211.05503/) (2022)
- [Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access](https://mlsanity.com/paper/2212.05373/) (2022)
- [Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema](https://mlsanity.com/paper/2303.16252/) (2023)
- [Revisiting Conversation Discourse for Dialogue Disentanglement](https://mlsanity.com/paper/2306.03975/) (2023)
- [Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking](https://mlsanity.com/paper/2302.08220/) (2023)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [Beyond Ontology in Dialogue State Tracking for Goal-Oriented Chatbot](https://mlsanity.com/paper/2410.22767/) (2024)
- [Dynamic Knowledge Fusion for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2603.10367/) (2026)

## Abstract (arXiv)

Dialogue State Tracking (DST) aims to keep track of users' intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under-studied problem. Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains. To address these issues, we propose a novel Dynamic Schema Graph Fusion Network (DSGFNet), which generates a dynamic schema graph to explicitly fuse the prior slot-domain membership relations and dialogue-aware dynamic slot relations. It also uses the schemata to facilitate knowledge transfer to new domains. DSGFNet consists of a dialogue utterance encoder, a schema graph encoder, a dialogue-aware schema graph evolving network, and a schema graph enhanced dialogue state decoder. Empirical results on benchmark datasets (i.e., SGD, MultiWOZ2.1, and MultiWOZ2.2), show that DSGFNet outperforms existing methods.