mlsanityData to 7 Oct 2026

Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation

arXiv 2012.02952 ↗First posted 5 Dec 2020cs.CL cs.LGcited by 47

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Abstract (arXiv)

Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which augments data through reinforcement learning guided conditional generation. We evaluate Data Boost on three diverse text classification tasks under five different classifier architectures. The result shows that Data Boost can boost the performance of classifiers especially in low-resource data scenarios. For instance, Data Boost improves F1 for the three tasks by 8.7% on average when given only 10% of the whole data for training. We also compare Data Boost with six prior text augmentation methods. Through human evaluations (N=178), we confirm that Data Boost augmentation has comparable quality as the original data with respect to readability and class consistency.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Dec 2020.

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Citing papers per year: 2021: 12; 2022: 9; 2023: 10; 2024: 10; 2025: 4; 2026: 2.

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  1. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  2. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  3. 2021FUDGE: Controlled Text Generation With Future Discriminatorscited by 374
  4. 2022A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Modelscited by 128
  5. 2023Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventionscited by 121
  6. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  7. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  8. 2021Mitigating Political Bias in Language Models Through Reinforced Calibrationcited by 73
  9. 2021AEDA: An Easier Data Augmentation Technique for Text Classificationcited by 67
  10. 2024A Comprehensive Survey on Data Augmentationcited by 38
  11. 2021Efficient (Soft) Q-Learning for Text Generation with Limited Good Datacited by 35
  12. 2023Training Models to Generate, Recognize, and Reframe Unhelpful Thoughtscited by 31
35 more
  1. 2022DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generationcited by 30
  2. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
  3. 2023BOLT: Fast Energy-based Controlled Text Generation with Tunable Biasescited by 24
  4. 2021GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentationcited by 20
  5. 2023Using GPT-4 to Augment Unbalanced Data for Automatic Scoringcited by 20
  6. 2023Adaptive and Personalized Exercise Generation for Online Language Learningcited by 18
  7. 2024ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Informationcited by 17
  8. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  9. 2022Recent Advances in Neural Text Generation: A Task-Agnostic Surveycited by 14
  10. 2022Knowledge Infused Decodingcited by 12
  11. 2022Non-Parallel Text Style Transfer with Self-Parallel Supervisioncited by 11
  12. 2022Improving Short Text Classification With Augmented Data Using GPT-3cited by 11
  13. 2021Revisiting Contextual Toxicity Detection in Conversationscited by 8
  14. 2022EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classificationcited by 7
  15. 2022PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learnerscited by 7
  16. 2024Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMscited by 7
  17. 2021Good-Enough Example Extrapolationcited by 5
  18. 2023"A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Predictioncited by 4
  19. 2024Animal Behavior Analysis Methods Using Deep Learning: A Surveycited by 4
  20. 2025Semantic Probabilistic Control of Language Modelscited by 4
  21. 2024Finding a Needle in the Adversarial Haystack: A Targeted Paraphrasing Approach For Uncovering Edge Cases with Minimal Distribution Distortioncited by 3
  22. 2023Mitigating Data Scarcity for Large Language Modelscited by 2
  23. 2023Data Augmentation for Neural NLPcited by 2
  24. 2023Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generationcited by 2
  25. 2024On Evaluation Protocols for Data Augmentation in a Limited Data Scenariocited by 2
  26. 2024A Framework for Real-time Safeguarding the Text Generation of Large Language Modelcited by 2
  27. 2025Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classificationcited by 2
  28. 2024DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustnesscited by 1
  29. 2024MapExplorer: New Content Generation from Low-Dimensional Visualizationscited by 1
  30. 2025The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Taskscited by 1
  31. 2023Reward Dropout Improves Control: Bi-objective Perspective on Reinforced LMcited by 0
  32. 2024Evaluation Metrics for Text Data Augmentation in NLPcited by 0
  33. 2025Automated Generation of Research Workflows from Academic Papers: A Full-text Mining Frameworkcited by 0
  34. 2026Leveraging Sentence-oriented Augmentation and Transformer-Based Architecture for Vietnamese-Bahnaric Translationcited by 0
  35. 2026Layering Virtual Try-Oncited by 0