Research and Application Status of Text Generation Tasks Based on Generative Adversarial Network

The chapter begins by introducing the importance of text generation in Natural Language Processing (NLP) and its wide applications, such as machine translation and automatic question answering. It then discusses the evolution of text generation methods from rule-based systems to deep learning models, focusing on the challenges and limitations of traditional approaches. The core of the chapter is the application of Generative Adversarial Networks (GANs) in text generation, including the unique challenges posed by discrete data like text. The authors review various GAN-based models and innovations designed to overcome these challenges, such as Wasserstein GAN, GSGAN, and SeqGAN. The chapter also highlights recent advancements like DP-GAN and SAL, which address issues like mode collapse and reward sparsity. Throughout, the chapter emphasizes the potential of GANs to generate diverse, high-quality text, and concludes with a discussion on the future trends and applications of this technology in the field of NLP.