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Neural Belief Tracker: Data-Driven Dialogue State Tracking

arXiv 1606.03777 ↗First posted 12 Jun 2016cs.CL cs.AI cs.LGcited by 142

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Abstract (arXiv)

One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users' language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.

Key facts. Cited by 142 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2016.

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Citing papers per year: 2018: 2; 2019: 0; 2020: 0; 2021: 43; 2022: 43; 2023: 20; 2024: 20; 2025: 5; 2026: 9.

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  1. 2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 203
  2. 2024A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systemscited by 170
  3. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  4. 2021Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue Systemcited by 139
  5. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  6. 2021GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injectioncited by 102
  7. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  8. 2021MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluationcited by 86
  9. 2022DialFRED: Dialogue-Enabled Agents for Embodied Instruction Followingcited by 63
  10. 2021DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendationcited by 46
  11. 2022Continual Prompt Tuning for Dialog State Trackingcited by 44
  12. 2022Controllable Dialogue Simulation with In-Context Learningcited by 42
38 more
  1. 2022InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuningcited by 39
  2. 2023ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?cited by 38
  3. 2021SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systemscited by 34
  4. 2022SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generationcited by 34
  5. 2024Large Language Models as Zero-shot Dialogue State Tracker through Function Callingcited by 34
  6. 2021Zero-shot Generalization in Dialog State Tracking through Generative Question Answeringcited by 33
  7. 2021Semi-Supervised Variational Reasoning for Medical Dialogue Generationcited by 30
  8. 2022A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learningcited by 30
  9. 2022Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialogcited by 30
  10. 2022HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Datacited by 28
  11. 2022SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understandingcited by 28
  12. 2021Slot Self-Attentive Dialogue State Trackingcited by 27
  13. 2024Can Large Language Models Understand Context?cited by 26
  14. 2024Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulationcited by 24
  15. 2021Pretraining the Noisy Channel Model for Task-Oriented Dialoguecited by 23
  16. 2022"Do you follow me?": A Survey of Recent Approaches in Dialogue State Trackingcited by 22
  17. 2022GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformerscited by 22
  18. 2022ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Formatcited by 22
  19. 2018Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learningcited by 21
  20. 2021DS-TOD: Efficient Domain Specialization for Task Oriented Dialogcited by 21
  21. 2021Amendable Generation for Dialogue State Trackingcited by 21
  22. 2023DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AIcited by 21
  23. 2021Effective Sequence-to-Sequence Dialogue State Trackingcited by 20
  24. 2021ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialoguescited by 20
  25. 2022Learning Dialogue Representations from Consecutive Utterancescited by 17
  26. 2024SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Trackingcited by 17
  27. 2021Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systemscited by 16
  28. 2022MVP: Multi-task Supervised Pre-training for Natural Language Generationcited by 16
  29. 2022Robust Dialogue State Tracking with Weak Supervision and Sparse Datacited by 15
  30. 2021DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialoguecited by 13
  31. 2021Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systemscited by 13
  32. 2022BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasetscited by 13
  33. 2024Common Ground Tracking in Multimodal Dialoguecited by 13
  34. 2018Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learningcited by 12
  35. 2021Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labelingcited by 11
  36. 2021Cross-lingual Intermediate Fine-tuning improves Dialogue State Trackingcited by 11
  37. 2022ASSIST: Towards Label Noise-Robust Dialogue State Trackingcited by 11
  38. 2022KETOD: Knowledge-Enriched Task-Oriented Dialoguecited by 11

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