Text Data Augmentation for Deep Learning

🤖 Plain-English Summary

Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. 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).

🔑 Key Findings

• 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.

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