A Survey of Data Augmentation Approaches for NLP
Abstract
Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks that require large amounts of training data. Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data. In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner. We first introduce and motivate data augmentation for NLP, and then discuss major methodologically representative approaches. Next, we highlight techniques that are used for popular NLP applications and tasks. We conclude by outlining current challenges and directions for future research. Overall, our paper aims to clarify the landscape of existing literature in data augmentation for NLP and motivate additional work in this area. We also present a GitHub repository with a paper list that will be continuously updated at https://github.com/styfeng/DataAug4NLP
Cite
@article{arxiv.2105.03075,
title = {A Survey of Data Augmentation Approaches for NLP},
author = {Steven Y. Feng and Varun Gangal and Jason Wei and Sarath Chandar and Soroush Vosoughi and Teruko Mitamura and Eduard Hovy},
journal= {arXiv preprint arXiv:2105.03075},
year = {2021}
}
Comments
Accepted to ACL 2021 Findings. GitHub repo with paper list at https://github.com/styfeng/DataAug4NLP ; Talk at https://www.youtube.com/watch?v=kNBVesKUZCk&ab_channel=StevenFeng ; Podcast at https://www.youtube.com/watch?v=qmqyT_97Poc&ab_channel=GradientFlow and https://thedataexchange.media/data-augmentation-in-natural-language-processing