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Collection of papers and resources for data augmentation for NLP.
| Date | Stars |
|---|---|
| 2026-07-24 | 835 |
| 2026-07-25 | 835 |
| 2026-07-28 | 835 |
| 2026-07-30 | 835 |
| 2026-08-06 | 835 |
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Momentum
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growth rate 0.00%/day
# Data Augmentation Techniques for NLP
If you'd like to add your paper, do not email us. Instead, read the protocol for [adding a new entry](https://github.com/styfeng/DataAug4NLP/blob/main/rules.md) and send a pull request.
We group the papers by [text classification](#text-classification), [translation](#translation), [summarization](#summarization), [question-answering](#question-answering), [sequence tagging](#sequence-tagging), [parsing](#parsing), [grammatical-error-correction](#grammatical-error-correction), [generation](#generation), [dialogue](#dialogue), [multimodal](#multimodal), [mitigating bias](#mitigating-bias), [mitigating class imbalance](#mitigating-class-imbalance), [adversarial examples](#adversarial-examples), [compositionality](#compositionality), and [automated augmentation](#automated-augmentation).
This repository is based on our paper, ["A survey of data augmentation approaches in NLP (Findings of ACL '21)"](https://aclanthology.org/2021.findings-acl.84/). You can cite it as follows:
```
@inproceedings{feng-etal-2021-survey,
title = "A Survey of Data Augmentation Approaches for {NLP}",
author = "Feng, Steven Y. and
Gangal, Varun and
Wei, Jason and
Chandar, Sarath and
Vosoughi, Soroush and
Mitamura, Teruko and
Hovy, Eduard",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.84",
doi = "10.18653/v1/2021.findings-acl.84",
pages = "968--988",
}
```
Authors: <a href="https://scholar.google.ca/citations?hl=en&user=zwiszZIAAAAJ">Steven Y. Feng</a>,
<a href="https://scholar.google.com/citations?user=rWZq2nQAAAAJ&hl=en">Varun Gangal</a>,
<a href="https://scholar.google.com/citations?user=wA5TK_0AAAAJ&hl=en">Jason Wei</a>,
<a href="https://scholar.google.co.in/citations?user=yxWtZLAAAAAJ&hl=en">Sarath Chandar</a>,
<a href="https://scholar.google.ca/citations?user=45DAXkwAAAAJ&hl=en">Soroush Vosoughi</a>,
<a href="https://scholar.google.com/citations?user=gjsxBCkAAAAJ&hl=en">Teruko Mitamura</a>,
<a href="https://scholar.google.com/citations?user=PUFxrroAAAAJ&hl=en">Eduard Hovy</a>
Special thanks to Ryan Shentu, Fiona Feng, Karen Liu, Emily Nie, Tanya Lu, and Bonnie Ma for helping out with this repo.
Note: WIP. More papers will be added from our survey paper to this repo soon.
Inquiries should be directed to [email protected] or by opening an issue here.
Also, check out our **talk for Google Research** (Steven Feng and Varun Gangal) [here](https://www.youtube.com/watch?v=kNBVesKUZCk&ab_channel=StevenFeng), and our **podcast episode** (Steven Feng and Eduard Hovy) [here](https://www.youtube.com/watch?v=qmqyT_97Poc) and [here](https://thedataexchange.media/data-augmentation-in-natural-language-processing/).
### Text Classification
| Paper | Datasets |
| -- | --- |
| Unsupervised Word Sense Disambiguation Rivaling Supervised Methods ([ACL '95](https://www.aclweb.org/anthology/P95-1026.pdf)) | Paper-Specific/Legacy Corpus |
| Synonym Replacement (Character-Level Convolutional Networks for Text Classification, [NeurIPS '15](https://papers.nips.cc/paper/2015/file/250cf8b51c773f3f8dc8b4be867a9a02-Paper.pdf)) | AG’s News, DBPedia, Yelp, Yahoo Answers, Amazon |
| That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets [(EMNLP '15)](https://www.aclweb.org/anthology/D15-1306.pdf) | twitter|
| Robust Training under Linguistic Adversity [(EACL '17)](https://www.aclweb.org/anthology/E17-2004/) [code](https://github.com/lrank/Linguistic_adversity) | Movie review, customer review, SUBJ, SST |
| Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations [(NAACL '18)](https://www.aExcerpt of 29,233 characters
Read on GitHub
46
Jason Wei · @BMIRDS
30
16
6
Shuhuai Ren · Peking University · China
2
1
1
1
1
Gerard de Melo · Hasso Plattner Institute · Germany
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1f2d084e32b4a73e, topic:natural-language-processing, topic:text-classification, readme:natural language processing
matched fp:1f2d084e32b4a73e, topic:data-augmentation, desc:data augmentation, readme:data augmentation
matched fp:1f2d084e32b4a73e, topic:deep-learning