Natural language

Microeconomics

Scarcity

Diversity (politics)

Telecommunications

Economics

Computer science

Anthropology

Filter (signal processing)

Natural language processing

Sampling (signal processing)

Sociology

Data science

Artificial intelligence

Deep learning

Machine learning

Computer vision

Frame (networking)

AI Open

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 helpful resources are provided in the appendix.

Data augmentation approaches in natural language processing: A survey