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Turn-Level Active Learning for Dialogue State Tracking

arXiv 2310.14513 ↗First posted 23 Oct 2023cs.CLcited by 0

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Abstract (arXiv)

Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems. However, collecting a large amount of turn-by-turn annotated dialogue data is costly and inefficient. In this paper, we propose a novel turn-level active learning framework for DST to actively select turns in dialogues to annotate. Given the limited labelling budget, experimental results demonstrate the effectiveness of selective annotation of dialogue turns. Additionally, our approach can effectively achieve comparable DST performance to traditional training approaches with significantly less annotated data, which provides a more efficient way to annotate new dialogue data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 41 references that are in the map. First posted on arXiv 23 Oct 2023.

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26 of 41 references.

References · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
  3. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
  4. 2019Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Datasetcited by 426
  5. 2021True Few-Shot Learning with Language Modelscited by 343
  6. 2019Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systemscited by 215
  7. 2020TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialoguecited by 185
  8. 2021Active Learning by Acquiring Contrastive Examplescited by 154
  9. 2021Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue Systemcited by 139
  10. 2020TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Trackingcited by 130
  11. 2020Cold-start Active Learning through Self-supervised Language Modelingcited by 128
  12. 2017Deep Active Learning for Named Entity Recognitioncited by 108
14 more
  1. 2021Dialogue State Tracking with a Language Model using Schema-Driven Promptingcited by 94
  2. 2017Learning how to Active Learn: A Deep Reinforcement Learning Approachcited by 79
  3. 2019Efficient Dialogue State Tracking by Selectively Overwriting Memorycited by 76
  4. 2020Reinforced active learning for image segmentationcited by 56
  5. 2021Zero-Shot Dialogue State Tracking via Cross-Task Transfercited by 52
  6. 2022Continual Prompt Tuning for Dialog State Trackingcited by 44
  7. 2019Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answeringcited by 39
  8. 2021Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Trackingcited by 35
  9. 2021Slot Self-Attentive Dialogue State Trackingcited by 27
  10. 2021Amendable Generation for Dialogue State Trackingcited by 21
  11. 2021Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Trackingcited by 20
  12. 2021Phrase-level Active Learning for Neural Machine Translationcited by 13
  13. 2021Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?cited by 5
  14. 2021Attention Guided Dialogue State Tracking with Sparse Supervisioncited by 4

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