Research Area:  Machine Learning

Abstract:

As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in many tasks. One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping the model to better generalize to unseen testing data. In this survey, we frame DA methods into three categories based on the diversity of augmented data, including paraphrasing, noising, and sampling. Our paper sets out to analyze DA methods in detail according to the above categories. Further, we also introduce their applications in NLP tasks as well as the challenges. Some useful resources are provided in Appendix A.

Keywords:  
Machine learning
Data augmentation
Natural language processing
Deep learning techniques
Text classification

Author(s) Name:  Bohan Li, Yutai Hou, Wanxiang Che

Publisher name:  Elsevier

DOI:  10.1016/j.aiopen.2022.03.001

Volume Information:  Volume 3

Paper Link:   https://www.sciencedirect.com/science/article/pii/S2666651022000080