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Multi-domain Dialog State Tracking using Recurrent Neural Networks

arXiv 1506.07190 ↗First posted 23 Jun 2015cs.CL cs.LGcited by 14

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Abstract (arXiv)

Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, well-defined domain in mind. This paper shows that dialog data drawn from different dialog domains can be used to train a general belief tracking model which can operate across all of these domains, exhibiting superior performance to each of the domain-specific models. We propose a training procedure which uses out-of-domain data to initialise belief tracking models for entirely new domains. This procedure leads to improvements in belief tracking performance regardless of the amount of in-domain data available for training the model.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jun 2015.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 2; 2024: 0; 2025: 0; 2026: 1.

14 papers.

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  1. 2021Multi-Task Learning in Natural Language Processing: An Overviewcited by 61
  2. 2021Slot Self-Attentive Dialogue State Trackingcited by 27
  3. 2022Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systemscited by 9
  4. 2022A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigmscited by 4
  5. 2023Diable: Efficient Dialogue State Tracking as Operations on Tablescited by 4
  6. 2021Ranking Micro-Influencers: a Novel Multi-Task Learning and Interpretable Frameworkcited by 2
  7. 2023Intelligent Assistant Language Understanding On Devicecited by 2
  8. 2021An empirical assessment of deep learning approaches to task-oriented dialog managementcited by 1
  9. 2022PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agentcited by 1
  10. 2022A Survey on Conversational Search and Applications in Biomedicinecited by 1
  11. 2022Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysiscited by 0
  12. 2022Recent Progress in Conversational AIcited by 0
2 more
  1. 2022Design Perspectives of Multitask Deep Learning Models and Applicationscited by 0
  2. 2026Candidate Attended Dialogue State Tracking Using BERTcited by 0