Abstract

In the field of Natural Language Processing (NLP), low-resource tasks—such as those involving underrepresented languages or domains with limited labeled data—pose significant challenges to model training and performance. Generative Adversarial Networks (GANs), originally popularized in computer vision, have been adapted for NLP to generate synthetic data that augments existing datasets. This article examines the role of GANs in data augmentation for low-resource NLP, highlighting their mechanisms, applications, advantages, implementation strategies, and potential limitations. By synthesizing realistic text samples, GANs help mitigate data scarcity, improve model generalization, and enhance performance in tasks like machine translation, sentiment analysis, and named entity recognition. Case studies from recent research demonstrate GANs' efficacy, while discussions on ethical considerations underscore the need for responsible deployment. Ultimately, GANs represent a promising avenue for democratizing NLP advancements in resource-constrained environments.