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A Survey of Data Augmentation Approaches for NLP

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2105.03075

Summary

Data augmentation (DA) adds variety to training data without collecting new examples. It is standard in computer vision but less developed in NLP. Text is discrete, so continuous noise cannot be applied, and it is harder to keep labels unchanged after a transformation. Feng et al. present what they call the first detailed survey of DA for NLP. They explain why DA is useful, the tension between how easy a method is to implement and how much it helps, and how little theory explains why it works. They group methods into three families: rule-based transforms, MixUp-style interpolation of examples, and model-based generation such as backtranslation or language-model word substitution. They then link DA to applications (low-resource languages, bias mitigation, class imbalance, few-shot learning, adversarial examples) and to tasks from summarization and QA to parsing, NMT, dialogue and multimodal work. The paper runs no new experiments. Its contribution is a structured overview, a list of open problems and a maintained paper list. This makes it a practical guide for choosing augmentation strategies in low-resource settings such as Ukrainian NLP.

Key points

  • Rule-based methods:
    • EDA uses random token insertion, deletion and swap.
    • Dependency tree morphing (Şahin and Steedman, 2018) rotates or crops a tree by swapping or removing sibling subtrees. It helps most for languages with rich case marking, such as Baltic and Slavic languages.
  • Interpolation (MSDA):
    • MixUp is applied to text by mixing embeddings or hidden layers.
    • SEQ2MIXUP's "soft" variant outperforms its "hard" variant and SwitchOut.
  • Model-based methods: backtranslation, DiPS paraphrasing, contextual replacement with an RNN LM (Kobayashi, 2018), G-DAUG^c with transformers, and BERT-based corrupt-and-reconstruct (Ng et al., 2020).
  • Trade-offs: Rule-based methods are cheap but usually give modest gains. Augmented data should be neither too close to nor too far from the original distribution. Kashefi and Hwa (2020) use a KL-divergence criterion to pick heuristics instead of testing all of them.
  • Open challenges and resources:
    • Challenges include span-based tasks, specialized domains, typologically distant low-resource languages, offline versus online DA, and the lack of unified benchmarks.
    • Recommended practices include reporting variance across random seeds.
    • The maintained paper list is at github.com/styfeng/DataAug4NLP.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

264 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 258 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2105.03075 · arXiv 2105.03075

@misc{feng2021survey,
  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},
  year = {2021},
  doi = {10.48550/arxiv.2105.03075},
  eprint = {2105.03075},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2105.03075}
}

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