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A Survey on Data Augmentation for Text Classification

arXiv 2107.03158 ↗First posted 7 Jul 2021cs.CL cs.AIcited by 111

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Abstract (arXiv)

Data augmentation, the artificial creation of training data for machine learning by transformations, is a widely studied research field across machine learning disciplines. While it is useful for increasing a model's generalization capabilities, it can also address many other challenges and problems, from overcoming a limited amount of training data, to regularizing the objective, to limiting the amount data used to protect privacy. Based on a precise description of the goals and applications of data augmentation and a taxonomy for existing works, this survey is concerned with data augmentation methods for textual classification and aims to provide a concise and comprehensive overview for researchers and practitioners. Derived from the taxonomy, we divide more than 100 methods into 12 different groupings and give state-of-the-art references expounding which methods are highly promising by relating them to each other. Finally, research perspectives that may constitute a building block for future work are provided.

Key facts. Cited by 111 papers in the map (data through 7 Oct 2026). Builds on 93 of its 195 references that are in the map. First posted on arXiv 7 Jul 2021.

Builds on

93 of 195 references.

References · 93

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  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
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38 more
  1. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
  2. 2019When Does Label Smoothing Help?cited by 985
  3. 2019AugMix: A Simple Data Processing Method to Improve Robustness and Uncertaintycited by 897
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  5. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
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  13. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
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  28. 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 185
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  38. 2018Synthetic Data Augmentation using GAN for Improved Liver Lesion Classificationcited by 136

50 most cited of 93 All 93 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 19; 2023: 34; 2024: 25; 2025: 20; 2026: 11.

111 papers.

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  1. 2023AugGPT: Leveraging ChatGPT for Text Data Augmentationcited by 128
  2. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  3. 2023A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4cited by 81
  4. 2023Boosting Adversarial Transferability by Achieving Flat Local Maximacited by 48
  5. 2024A Comprehensive Survey on Data Augmentationcited by 38
  6. 2022CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domaincited by 31
  7. 2022A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainabilitycited by 30
  8. 2023A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluationcited by 24
  9. 2023Image Data Augmentation Approaches: A Comprehensive Survey and Future directionscited by 23
  10. 2024Application of LLM Agents in Recruitment: A Novel Framework for Resume Screeningcited by 23
  11. 2025Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunitiescited by 21
  12. 2023Using GPT-4 to Augment Unbalanced Data for Automatic Scoringcited by 20
38 more
  1. 2021Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLPcited by 17
  2. 2024Empowering Large Language Models for Textual Data Augmentationcited by 17
  3. 2023Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learningcited by 16
  4. 2022Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoderscited by 15
  5. 2023Simple Disentanglement of Style and Content in Visual Representationscited by 10
  6. 2023Optimizing PatchCore for Few/many-shot Anomaly Detectioncited by 10
  7. 2023It Is All About Data: A Survey on the Effects of Data on Adversarial Robustnesscited by 9
  8. 2023Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigmcited by 9
  9. 2024Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Studycited by 9
  10. 2024Improving Black-box Robustness with In-Context Rewritingcited by 8
  11. 2022PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learnerscited by 7
  12. 2024Exploration of Masked and Causal Language Modelling for Text Generationcited by 7
  13. 2023Data Optimization in Deep Learning: A Surveycited by 6
  14. 2023From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysiscited by 6
  15. 2024Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?cited by 6
  16. 2024Exploring Empty Spaces: Human-in-the-Loop Data Augmentationcited by 6
  17. 2024Delta: A Cloud-assisted Data Enrichment Framework for On-Device Continual Learningcited by 6
  18. 2025AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecturecited by 6
  19. 2022Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbationscited by 5
  20. 2022General Cyclical Training of Neural Networkscited by 5
  21. 2022Data Augmentation for Biomedical Factoid Question Answeringcited by 5
  22. 2022Selective Text Augmentation with Word Roles for Low-Resource Text Classificationcited by 5
  23. 2025Explainable AI: XAI-Guided Context-Aware Data Augmentationcited by 5
  24. 2022StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfercited by 4
  25. 2023Distributional Data Augmentation Methods for Low Resource Languagecited by 4
  26. 2026Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligencecited by 4
  27. 2022Highly Generalizable Models for Multilingual Hate Speech Detectioncited by 3
  28. 2022Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligencecited by 3
  29. 2022Human-in-the-Loop Hate Speech Classification in a Multilingual Contextcited by 3
  30. 2023Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairscited by 3
  31. 2023Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Modelscited by 3
  32. 2023TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Miningcited by 3
  33. 2023LowResource at BLP-2023 Task 2: Leveraging BanglaBert for Low Resource Sentiment Analysis of Bangla Languagecited by 3
  34. 2023RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendationcited by 3
  35. 2024Detecting misinformation through Framing Theory: the Frame Element-based Modelcited by 3
  36. 2024Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolutioncited by 3
  37. 2024Evaluating the Impact of Data Augmentation on Predictive Model Performancecited by 3
  38. 2023Data Augmentation for Neural NLPcited by 2

50 most cited of 111 All 111 (JSON)