A Survey of Data Augmentation Approaches for NLP
Citations Over TimeTop 1% of 2021 papers
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
Related Papers
- → Text Data Augmentation for Deep Learning(2021)1,648 cited
- → Natural Language Processing for Requirements Engineering(2021)282 cited
- Improving Explainability and Accuracy through Feature Engineering: A Taxonomy of Features in NLP-based Machine Learning(2021)
- → Opening the NLP Blackbox - Analysis and Evaluation of NLP Models(2020)3 cited
- → Natural Language Processing (NLP) for Requirements Engineering: A Systematic Mapping Study Dataset(2020)3 cited