mlsanityData to 14 Oct 2026

Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning

arXiv 2301.10915 ↗First posted 26 Jan 2023cs.CL cs.AIcited by 8

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Abstract (arXiv)

Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs. Existing works fine-tune all language model (LM) parameters to tackle the DST task, which requires significant data and computing resources for training and hosting. The cost grows exponentially in the real-world deployment where dozens of fine-tuned LM are used for different domains and tasks. To reduce parameter size and better utilize cross-task shared information, we propose to use soft prompt token embeddings to learn task properties. Without tuning LM parameters, our method drastically reduces the number of parameters needed to less than 0.5% of prior works while achieves better low-resource DST performance.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 45 of its 60 references that are in the map. First posted on arXiv 26 Jan 2023.

Builds on

45 of 60 references.

References · 45

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  3. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  4. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
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  6. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  7. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 766
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  11. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
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33 more
  1. 2020AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Promptscited by 249
  2. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  3. 2021PPT: Pre-trained Prompt Tuning for Few-shot Learningcited by 232
  4. 2019Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systemscited by 216
  5. 2020MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselinescited by 204
  6. 2020TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialoguecited by 185
  7. 2021Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue Systemcited by 139
  8. 2020MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systemscited by 103
  9. 2021Dialogue State Tracking with a Language Model using Schema-Driven Promptingcited by 94
  10. 2021DEGREE: A Data-Efficient Generation-Based Event Extraction Modelcited by 83
  11. 2020Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Trackingcited by 65
  12. 2019Dialog State Tracking: A Neural Reading Comprehension Approachcited by 62
  13. 2021Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Trackingcited by 61
  14. 2021Zero-Shot Dialogue State Tracking via Cross-Task Transfercited by 52
  15. 2022Continual Prompt Tuning for Dialog State Trackingcited by 44
  16. 2020From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gapcited by 40
  17. 2021Counter-Interference Adapter for Multilingual Machine Translationcited by 40
  18. 2019Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answeringcited by 39
  19. 2021Zero-shot Generalization in Dialog State Tracking through Generative Question Answeringcited by 33
  20. 2022Summarization as Indirect Supervision for Relation Extractioncited by 31
  21. 2021CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systemscited by 30
  22. 2021Slot Self-Attentive Dialogue State Trackingcited by 27
  23. 2018Zero-Shot Adaptive Transfer for Conversational Language Understandingcited by 20
  24. 2021Effective Sequence-to-Sequence Dialogue State Trackingcited by 20
  25. 2022DICE: Data-Efficient Clinical Event Extraction with Generative Modelscited by 20
  26. 2021Few Shot Dialogue State Tracking using Meta-learningcited by 16
  27. 2022Show, Don't Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialoguecited by 16
  28. 2022Few-Shot Learning with Siamese Networks and Label Tuningcited by 15
  29. 2022Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?cited by 13
  30. 2022Multi-hop Evidence Retrieval for Cross-document Relation Extractioncited by 13
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  33. 2019Implicit Discourse Relation Identification for Open-domain Dialoguescited by 8

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 4; 2025: 2.

8 papers.

  1. 2023Towards LLM-driven Dialogue State Trackingcited by 25
  2. 2024Improving Event Definition Following For Zero-Shot Event Detectioncited by 7
  3. 2023STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Modelscited by 6
  4. 2024Zero-Shot Cross-Domain Dialogue State Tracking via Dual Low-Rank Adaptationcited by 5
  5. 2024Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Trackingcited by 2
  6. 2024An Approach to Build Zero-Shot Slot-Filling System for Industry-Grade Conversational Assistantscited by 1
  7. 2025Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selectioncited by 1
  8. 2025From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understandingcited by 0