mlsanityData to 7 Oct 2026

AEDA: An Easier Data Augmentation Technique for Text Classification

arXiv 2108.13230 ↗First posted 30 Aug 2021cs.CLcited by 67

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Abstract (arXiv)

This paper proposes AEDA (An Easier Data Augmentation) technique to help improve the performance on text classification tasks. AEDA includes only random insertion of punctuation marks into the original text. This is an easier technique to implement for data augmentation than EDA method (Wei and Zou, 2019) with which we compare our results. In addition, it keeps the order of the words while changing their positions in the sentence leading to a better generalized performance. Furthermore, the deletion operation in EDA can cause loss of information which, in turn, misleads the network, whereas AEDA preserves all the input information. Following the baseline, we perform experiments on five different datasets for text classification. We show that using the AEDA-augmented data for training, the models show superior performance compared to using the EDA-augmented data in all five datasets. The source code is available for further study and reproduction of the results.

Key facts. Cited by 67 papers in the map (data through 7 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 30 Aug 2021.

Builds on

18 of 29 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  4. 2019TinyBERT: Distilling BERT for Natural Language Understandingcited by 1,153
  5. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  6. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  7. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  8. 2020BAE: BERT-based Adversarial Examples for Text Classificationcited by 316
  9. 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
  10. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
  11. 2019Good-Enough Compositional Data Augmentationcited by 158
  12. 2016Recurrent Neural Network for Text Classification with Multi-Task Learningcited by 144
6 more
  1. 2017Data Augmentation for Low-Resource Neural Machine Translationcited by 139
  2. 2018Conditional BERT Contextual Augmentationcited by 107
  3. 2017Data Noising as Smoothing in Neural Network Language Modelscited by 78
  4. 2020Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generationcited by 47
  5. 2019Learning Data Manipulation for Augmentation and Weightingcited by 44
  6. 2020Adversarial Training for Aspect-Based Sentiment Analysis with BERTcited by 26

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 15; 2023: 12; 2024: 30; 2025: 6; 2026: 4.

67 papers.

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  1. 2024A Comprehensive Survey on Data Augmentationcited by 38
  2. 2022Weakly Supervised Data Augmentation Through Prompting for Dialogue Understandingcited by 32
  3. 2024Assessing Adversarial Robustness of Large Language Models: An Empirical Studycited by 21
  4. 2022Contrastive Learning for Prompt-Based Few-Shot Language Learnerscited by 17
  5. 2022Learning Multimodal Data Augmentation in Feature Spacecited by 17
  6. 2024Empowering Large Language Models for Textual Data Augmentationcited by 17
  7. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  8. 2025ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math Problem Solvingcited by 13
  9. 2024CoDa: Constrained Generation based Data Augmentation for Low-Resource NLPcited by 9
  10. 2024LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classificationcited by 8
  11. 2024ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptionscited by 8
  12. 2024A Survey of Data Synthesis Approachescited by 8
38 more
  1. 2024LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?cited by 8
  2. 2022EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classificationcited by 7
  3. 2023Bag of Tricks for In-Distribution Calibration of Pretrained Transformerscited by 7
  4. 2024Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Datacited by 7
  5. 2025Diversity-oriented Data Augmentation with Large Language Modelscited by 7
  6. 2024Does Incomplete Syntax Influence Korean Language Model? Focusing on Word Order and Case Markerscited by 6
  7. 2024Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Datacited by 6
  8. 2022Embedding Hallucination for Few-Shot Language Fine-tuningcited by 5
  9. 2024Human-in-the-Loop Synthetic Text Data Inspection with Provenance Trackingcited by 5
  10. 2024An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classificationcited by 5
  11. 2025Limited Effectiveness of LLM-based Data Augmentation for COVID-19 Misinformation Stance Detectioncited by 5
  12. 2024Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generationcited by 4
  13. 2025Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarkingcited by 4
  14. 2022Prompt-based System for Personality and Interpersonal Reactivity Predictioncited by 3
  15. 2022HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms Detectioncited by 3
  16. 2022Robust Self-Augmentation for Named Entity Recognition with Meta Reweightingcited by 3
  17. 2022Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representationscited by 3
  18. 2023Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairscited by 3
  19. 2024Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modelingcited by 3
  20. 2024SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labelscited by 3
  21. 2024Evaluating Durability: Benchmark Insights into Multimodal Watermarkingcited by 3
  22. 2024Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Textscited by 3
  23. 2024Buster: Implanting Semantic Backdoor into Text Encoder to Mitigate NSFW Content Generationcited by 3
  24. 2022Sibylvariant Transformations for Robust Text Classificationcited by 2
  25. 2023Mitigating Data Scarcity for Large Language Modelscited by 2
  26. 2023Data Augmentation for Neural NLPcited by 2
  27. 2023Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalizationcited by 2
  28. 2024Unsupervised hard Negative Augmentation for contrastive learningcited by 2
  29. 2024On Evaluation Protocols for Data Augmentation in a Limited Data Scenariocited by 2
  30. 2024ARMADA: Attribute-Based Multimodal Data Augmentationcited by 2
  31. 2024Enhancing elusive clues in knowledge learning by contrasting attention of language modelscited by 2
  32. 2022BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Frameworkcited by 1
  33. 2022Dictionary-Assisted Supervised Contrastive Learningcited by 1
  34. 2023How to choose "Good" Samples for Text Data Augmentationcited by 1
  35. 2023CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identificationcited by 1
  36. 2024AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimescited by 1
  37. 2024Is Less More? Quality, Quantity and Context in Idiom Processing with Natural Language Modelscited by 1
  38. 2024Speak & Spell: LLM-Driven Controllable Phonetic Error Augmentation for Robust Dialogue State Trackingcited by 1

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