# Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking

arXiv: https://arxiv.org/abs/2004.03386 · HTML: https://mlsanity.com/paper/2004.03386/

## Key facts

- Cited by 27 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 27 of 27)

- [META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI](https://mlsanity.com/paper/2205.11029/) (2022)
- [ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser](https://mlsanity.com/paper/2104.04689/) (2021)
- [Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking](https://mlsanity.com/paper/2106.00291/) (2021)
- [Slot Self-Attentive Dialogue State Tracking](https://mlsanity.com/paper/2101.09374/) (2021)
- [Amendable Generation for Dialogue State Tracking](https://mlsanity.com/paper/2110.15659/) (2021)
- [Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking](https://mlsanity.com/paper/2104.04466/) (2021)
- [Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2204.06677/) (2022)
- [LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching](https://mlsanity.com/paper/2102.12671/) (2021)
- [Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL](https://mlsanity.com/paper/2106.02282/) (2021)
- [Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations](https://mlsanity.com/paper/2107.05168/) (2021)
- [LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking](https://mlsanity.com/paper/2205.02550/) (2022)
- [Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking](https://mlsanity.com/paper/2205.10059/) (2022)
- [Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?](https://mlsanity.com/paper/2105.03571/) (2021)
- [Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking](https://mlsanity.com/paper/2203.03123/) (2022)
- [TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages](https://mlsanity.com/paper/2205.06435/) (2022)
- [Diable: Efficient Dialogue State Tracking as Operations on Tables](https://mlsanity.com/paper/2305.17020/) (2023)
- [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)
- [Chain of Thought Explanation for Dialogue State Tracking](https://mlsanity.com/paper/2403.04656/) (2024)
- [TSCAN : Dialog Structure discovery using SCAN](https://mlsanity.com/paper/2107.06426/) (2021)
- [Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking](https://mlsanity.com/paper/2302.08220/) (2023)
- [Introducing "Forecast Utterance" for Conversational Data Science](https://mlsanity.com/paper/2309.03877/) (2023)
- [Ontology-Enhanced Slot Filling](https://mlsanity.com/paper/2108.11275/) (2021)
- [DSTEA: Improving Dialogue State Tracking via Entity Adaptive Pre-training](https://mlsanity.com/paper/2207.03858/) (2022)
- [Schema Encoding for Transferable Dialogue State Tracking](https://mlsanity.com/paper/2210.02351/) (2022)
- [Lexical Entrainment for Conversational Systems](https://mlsanity.com/paper/2310.09651/) (2023)
- [Beyond Ontology in Dialogue State Tracking for Goal-Oriented Chatbot](https://mlsanity.com/paper/2410.22767/) (2024)

## Abstract (arXiv)

Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can obtain new state-of-the-art performance of the open-vocabulary DST on both MultiWOZ 2.0 and MultiWOZ 2.1 benchmarks.