Data augmentation approaches in natural language processing: A survey

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