mlsanityData to 7 Oct 2026

Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding

arXiv 2210.14169 ↗First posted 25 Oct 2022cs.CL cs.AI cs.LGcited by 32

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Abstract (arXiv)

Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier, we explore few-shot data augmentation for dialogue understanding by prompting large pre-trained language models and present a novel approach that iterates on augmentation quality by applying weakly-supervised filters. We evaluate our methods on the emotion and act classification tasks in DailyDialog and the intent classification task in Facebook Multilingual Task-Oriented Dialogue. Models fine-tuned on our augmented data mixed with few-shot ground truth data are able to approach or surpass existing state-of-the-art performance on both datasets. For DailyDialog specifically, using 10% of the ground truth data we outperform the current state-of-the-art model which uses 100% of the data.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 42 of its 57 references that are in the map. First posted on arXiv 25 Oct 2022.

Builds on

42 of 57 references.

References · 42

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
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30 more
  1. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  2. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
  3. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
  4. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
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  7. 2021True Few-Shot Learning with Language Modelscited by 343
  8. 2021Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLPcited by 336
  9. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
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  13. 2018Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialogcited by 124
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  21. 2020Paraphrase Augmented Task-Oriented Dialog Generationcited by 43
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  26. 2020Strength from Weakness: Fast Learning Using Weak Supervisioncited by 18
  27. 2021Speaker Turn Modeling for Dialogue Act Classificationcited by 18
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Citing papers per year: 2022: 2; 2023: 11; 2024: 13; 2025: 5; 2026: 1.

32 papers.

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  1. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  2. 2022SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualizationcited by 166
  3. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 96
  4. 2023PLACES: Prompting Language Models for Social Conversation Synthesiscited by 72
  5. 2024Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometricscited by 56
  6. 2023Is ChatGPT Equipped with Emotional Dialogue Capabilities?cited by 54
  7. 2024Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Trainingcited by 53
  8. 2024What is the Role of Small Models in the LLM Era: A Surveycited by 41
  9. 2022In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Modelscited by 36
  10. 2023Controllable Mixed-Initiative Dialogue Generation through Promptingcited by 32
  11. 2023Mixture of Soft Prompts for Controllable Data Generationcited by 23
  12. 2024A Survey on Recent Advances in Conversational Data Generationcited by 23
20 more
  1. 2024Contextualization Distillation from Large Language Model for Knowledge Graph Completioncited by 18
  2. 2025nvBench 2.0: Resolving Ambiguity in Text-to-Visualization through Stepwise Reasoningcited by 17
  3. 2024The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trendscited by 11
  4. 2023Curriculum-Driven Edubot: A Framework for Developing Language Learning Chatbots Through Synthesizing Conversational Datacited by 9
  5. 2024Synthetic Data Generation with Large Language Models for Personalized Community Question Answeringcited by 8
  6. 2024PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traitscited by 6
  7. 2024Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcriptscited by 6
  8. 2025Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Promptscited by 5
  9. 2025We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourismcited by 5
  10. 2023Pre-Finetuning for Few-Shot Emotional Speech Recognitioncited by 4
  11. 2023MixPro: Simple yet Effective Data Augmentation for Prompt-based Learningcited by 4
  12. 2024Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generationcited by 4
  13. 2023Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Modelscited by 3
  14. 2024GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learningcited by 3
  15. 2024Data-Centric Improvements for Enhancing Multi-Modal Understanding in Spoken Conversation Modelingcited by 3
  16. 2023Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approachcited by 1
  17. 2025SpeechDialogueFactory: Generating High-Quality Speech Dialogue Data to Accelerate Your Speech-LLM Developmentcited by 1
  18. 2025Exploring Automated Recognition of Instructional Activity and Discourse from Multimodal Classroom Datacited by 1
  19. 2024CALICO: Conversational Agent Localization via Synthetic Data Generationcited by 0
  20. 2026Cross-Lingual Stability of LLM Judges Under Controlled Generation: Evidence from Finno-Ugric Languagescited by 0