# AEDA: An Easier Data Augmentation Technique for Text Classification

arXiv: https://arxiv.org/abs/2108.13230 · HTML: https://mlsanity.com/paper/2108.13230/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [BAE: BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2004.01970/) (2020)
- [Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://mlsanity.com/paper/1805.06201/) (2018)
- [Data Augmentation using Pre-trained Transformer Models](https://mlsanity.com/paper/2003.02245/) (2020)
- [Good-Enough Compositional Data Augmentation](https://mlsanity.com/paper/1904.09545/) (2019)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [Conditional BERT Contextual Augmentation](https://mlsanity.com/paper/1812.06705/) (2018)
- [Data Noising as Smoothing in Neural Network Language Models](https://mlsanity.com/paper/1703.02573/) (2017)
- [Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation](https://mlsanity.com/paper/2012.02952/) (2020)
- [Learning Data Manipulation for Augmentation and Weighting](https://mlsanity.com/paper/1910.12795/) (2019)
- [Adversarial Training for Aspect-Based Sentiment Analysis with BERT](https://mlsanity.com/paper/2001.11316/) (2020)

## Built on by (most cited first, 30 of 67)

- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding](https://mlsanity.com/paper/2210.14169/) (2022)
- [Assessing Adversarial Robustness of Large Language Models: An Empirical Study](https://mlsanity.com/paper/2405.02764/) (2024)
- [Learning Multimodal Data Augmentation in Feature Space](https://mlsanity.com/paper/2212.14453/) (2022)
- [Contrastive Learning for Prompt-Based Few-Shot Language Learners](https://mlsanity.com/paper/2205.01308/) (2022)
- [Empowering Large Language Models for Textual Data Augmentation](https://mlsanity.com/paper/2404.17642/) (2024)
- [TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding](https://mlsanity.com/paper/2205.06153/) (2022)
- [ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math Problem Solving](https://mlsanity.com/paper/2501.08203/) (2025)
- [CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP](https://mlsanity.com/paper/2404.00415/) (2024)
- [A Survey of Data Synthesis Approaches](https://mlsanity.com/paper/2407.03672/) (2024)
- [LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification](https://mlsanity.com/paper/2402.16515/) (2024)
- [ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions](https://mlsanity.com/paper/2406.04286/) (2024)
- [LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?](https://mlsanity.com/paper/2408.16502/) (2024)
- [EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification](https://mlsanity.com/paper/2204.11205/) (2022)
- [Bag of Tricks for In-Distribution Calibration of Pretrained Transformers](https://mlsanity.com/paper/2302.06690/) (2023)
- [Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data](https://mlsanity.com/paper/2410.02056/) (2024)
- [Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data](https://mlsanity.com/paper/2412.16243/) (2024)
- [Diversity-oriented Data Augmentation with Large Language Models](https://mlsanity.com/paper/2502.11671/) (2025)
- [Limited Effectiveness of LLM-based Data Augmentation for COVID-19 Misinformation Stance Detection](https://mlsanity.com/paper/2503.02328/) (2025)
- [Does Incomplete Syntax Influence Korean Language Model? Focusing on Word Order and Case Markers](https://mlsanity.com/paper/2407.09184/) (2024)
- [Embedding Hallucination for Few-Shot Language Fine-tuning](https://mlsanity.com/paper/2205.01307/) (2022)
- [Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking](https://mlsanity.com/paper/2404.18881/) (2024)
- [An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification](https://mlsanity.com/paper/2409.03203/) (2024)
- [Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modeling](https://mlsanity.com/paper/2401.01830/) (2024)
- [Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation](https://mlsanity.com/paper/2404.00361/) (2024)
- [Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking](https://mlsanity.com/paper/2508.19558/) (2025)
- [Prompt-based System for Personality and Interpersonal Reactivity Prediction](https://mlsanity.com/paper/2203.12481/) (2022)
- [HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms Detection](https://mlsanity.com/paper/2204.06145/) (2022)
- [Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting](https://mlsanity.com/paper/2204.11406/) (2022)
- [Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations](https://mlsanity.com/paper/2211.08794/) (2022)

## 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.