ArticleJournal of big data2021
Text Data Augmentation for Deep Learning.
Article in Journal of big data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,807 papers, 1 of them a synthesis that pooled it.
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Who cites it
1,807 citing papers in PubMed, 1 synthesis or guideline pooled it, 1,649 citations in OpenAlex.
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- MorphoNet: An Interpretable Hierarchical Deep Learning Framework for Multi-Class Skin Lesion Classification Using Dermoscopic Morphology.Bioengineering (Basel, Switzerland) · 2026Article
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction.Journal of cardiovascular imaging · 2026Article
- Deep learning for precision grading of Schistosoma japonicum-induced liver fibrosis in ultrasound images.Nature communications · 2026Article
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1,747 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data. Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We highlight studies that cover how augmentations can construct test sets for generalization. NLP is at an early stage in applying Data Augmentation compared to Computer Vision. We highlight the key differences and promising ideas that have yet to be tested in NLP. For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. Finally, we discuss interesting topics around Data Augmentation in NLP such as task-specific augmentations, the use of prior knowledge in self-supervised learning versus Data Augmentation, intersections with transfer and multi-task learning, and ideas for AI-GAs (AI-Generating Algorithms). We hope this paper inspires further research interest in Text Data Augmentation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.