Data Augmentation Approaches in Natural Language Processing: A Survey

TL;DR

This survey reviews data augmentation techniques in NLP, categorizing them into paraphrasing, noising, and sampling, highlighting their applications, challenges, and potential to improve model generalization.

Contribution

It provides a comprehensive analysis of NLP data augmentation methods categorized by diversity, with insights into their applications and challenges.

Findings

DA methods improve NLP model generalization

Paraphrasing, noising, sampling are key categories

Challenges include maintaining data quality and diversity

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 helpful resources are provided in the appendix.