Text Data Augmentation for Deep Learning

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).

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 In this survey, we consider how the Data Augmentation training strategy can aid in its development.
• 2 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.
• 3 We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source: