A Review of Generative Adversarial Networks in Text Generation

Abstract

This paper presents a high-level exploration of Generative Adversarial Networks (GANs) and their potential role in the field of text generation, as well as their advancement in the past few years. I start by discussing the intricacies of natural language processing (NLP), specifically text generation, and the various challenges the field faces. I then dive into a detailed examination of various GAN models, each defined by its unique architecture and approach to overcoming the hurdles in text generation. I provide in-depth analyses of these key models, examining their strengths, limitations, and the specific text generation challenges they address. Furthermore, the paper identifies crucial issues in current GAN techniques, such as training instability and lack of output diversity. In response, I propose potential paths for future research, including the exploration of more compact and efficient GAN models. My conclusion highlights the significant potential of GANs in revolutionizing text generation, emphasizing their role in advancing AI's creative capabilities in language. This research not only serves as a valuable resource for those interested in the technical aspects of GANs but also acts as a gateway for future innovations in the rapidly evolving landscape of AI-driven text generation.