Data Augmentation for Neural NLP
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Abstract (arXiv)
Data scarcity is a problem that occurs in languages and tasks where we do not have large amounts of labeled data but want to use state-of-the-art models. Such models are often deep learning models that require a significant amount of data to train. Acquiring data for various machine learning problems is accompanied by high labeling costs. Data augmentation is a low-cost approach for tackling data scarcity. This paper gives an overview of current state-of-the-art data augmentation methods used for natural language processing, with an emphasis on methods for neural and transformer-based models. Furthermore, it discusses the practical challenges of data augmentation, possible mitigations, and directions for future research.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 56 of its 78 references that are in the map. First posted on arXiv 22 Feb 2023.
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56 of 78 references.
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