Research Area:  Machine Learning
Data augmentation, the artificial creation of training data for machine learning by transformations, is a widely studied research field across machine learning disciplines. While it is useful for increasing a model-s generalization capabilities, it can also address many other challenges and problems, from overcoming a limited amount of training data, to regularizing the objective, to limiting the amount data used to protect privacy. Based on a precise description of the goals and applications of data augmentation and a taxonomy for existing works, this survey is concerned with data augmentation methods for textual classification and aims to provide a concise and comprehensive overview for researchers and practitioners. Derived from the taxonomy, we divide more than 100 methods into 12 different groupings and give state-of-the-art references expounding which methods are highly promising by relating them to each other. Finally, research perspectives that may constitute a building block for future work are provided.
Natural language processing
Author(s) Name:  Markus Bayer , Marc-André Kaufhold , Christian Reuter
Journal name:  ACM Computing Surveys
Publisher name:  ACM
Volume Information:  volume 55
Paper Link:   https://dl.acm.org/doi/abs/10.1145/3544558