mlsanityData to 7 Oct 2026

MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking

arXiv 2404.08559 ↗First posted 12 Apr 2024cs.CLcited by 1

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Abstract (arXiv)

Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets. Previous zero-shot DST models mainly suffer from domain transferring and partial prediction problems. To address these challenges, we propose Mixture of Prefix Experts (MoPE) to establish connections between similar slots in different domains, which strengthens the model transfer performance in unseen domains. Empirical results demonstrate that MoPE-DST achieves the joint goal accuracy of 57.13% on MultiWOZ2.1 and 55.40% on SGD.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 12 Apr 2024.

Builds on

15 of 30 references.

References · 15

  1. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  2. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
  3. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
  4. 2019Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Datasetcited by 426
  5. 2019Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systemscited by 215
  6. 2020TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Trackingcited by 130
  7. 2019Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Trackingcited by 75
  8. 2019SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Trackingcited by 63
  9. 2019Dialog State Tracking: A Neural Reading Comprehension Approachcited by 62
  10. 2021Zero-Shot Dialogue State Tracking via Cross-Task Transfercited by 52
  11. 2022Continual Prompt Tuning for Dialog State Trackingcited by 44
  12. 2023ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?cited by 38
3 more
  1. 2020Non-Autoregressive Dialog State Trackingcited by 15
  2. 2023Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Trackingcited by 10
  3. 2023Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptationcited by 7

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DSTcited by 0