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EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

arXiv 1901.11196 ↗First posted 31 Jan 2019cs.CLcited by 800

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Abstract (arXiv)

We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.

Key facts. Cited by 800 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2019.

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Citing papers per year: 2021: 140; 2022: 182; 2023: 187; 2024: 145; 2025: 89; 2026: 57.

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  1. 2022Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference timecited by 1,366
  2. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  3. 2021Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigmcited by 405
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  6. 2023A Survey of Confidence Estimation and Calibration in Large Language Modelscited by 253
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  9. 2022Learning Video Representations from Large Language Modelscited by 204
  10. 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 185
  11. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  12. 2023AugGPT: Leveraging ChatGPT for Text Data Augmentationcited by 128
38 more
  1. 2023Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventionscited by 121
  2. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  3. 2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
  4. 2021Robustness Gym: Unifying the NLP Evaluation Landscapecited by 107
  5. 2023Text Classification via Large Language Modelscited by 104
  6. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  7. 2023Waffling around for Performance: Visual Classification with Random Words and Broad Conceptscited by 100
  8. 2023Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluationscited by 96
  9. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  10. 2023CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-trainingcited by 81
  11. 2021Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Testscited by 77
  12. 2021Towards Zero-Label Language Learningcited by 76
  13. 2023Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmarkcited by 75
  14. 2021Understanding by Understanding Not: Modeling Negation in Language Modelscited by 73
  15. 2021ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embeddingcited by 73
  16. 2021NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentationcited by 72
  17. 2022Teaching Structured Vision&Language Concepts to Vision&Language Modelscited by 71
  18. 2021Vision-Language Navigation with Random Environmental Mixupcited by 70
  19. 2024LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancementcited by 69
  20. 2023VeCLIP: Improving CLIP Training via Visual-enriched Captionscited by 68
  21. 2021AEDA: An Easier Data Augmentation Technique for Text Classificationcited by 67
  22. 2024Fine-Tuned 'Small' LLMs (Still) Significantly Outperform Zero-Shot Generative AI Models in Text Classificationcited by 66
  23. 2023CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learningcited by 62
  24. 2022Graph Data Augmentation for Graph Machine Learning: A Surveycited by 61
  25. 2022MixGen: A New Multi-Modal Data Augmentationcited by 61
  26. 2025Synthetic Data Generation Using Large Language Models: Advances in Text and Codecited by 61
  27. 2022RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extractioncited by 60
  28. 2022Twin Contrastive Learning for Online Clusteringcited by 60
  29. 2023Multiscale Positive-Unlabeled Detection of AI-Generated Textscited by 59
  30. 2022A Close Look into the Calibration of Pre-trained Language Modelscited by 57
  31. 2021Contrastive Self-supervised Sequential Recommendation with Robust Augmentationcited by 56
  32. 2023VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Modelscited by 55
  33. 2022UniCLIP: Unified Framework for Contrastive Language-Image Pre-trainingcited by 54
  34. 2023Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learningcited by 53
  35. 2022PromDA: Prompt-based Data Augmentation for Low-Resource NLU Taskscited by 51
  36. 2022Multi-VALUE: A Framework for Cross-Dialectal English NLPcited by 51
  37. 2023ViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistencycited by 50
  38. 2022Data Augmentation for Intent Classification with Off-the-shelf Large Language Modelscited by 49

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