A survey on text generation using generative adversarial networks. (November 2021)

• Record Type: Journal Article
• Authors: de Rosa, Gustavo H.; Papa, João P.
• Is Part Of: Pattern recognition. Volume 119(2021)
• Publication Date: 2021-11
• Subjects: Text generation -- Generative adversarial Networks -- Machine learning -- Language modeling -- Natural language processing; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108098
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Surveys and discusses the advancements in GAN-based text generation. Composed of thirty-five papers about GAN-based text generation. Provides a unique source of recent GAN-based text generation researches. All surveyed works ranges from 2016 to 2020. Abstract: This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called "natural" language. Nevertheless, adversarial text generation is not a simple task as its foremost architecture, the Generative Adversarial Networks, were designed to cope with continuous information (image) instead of discrete data (text). Thus, most works are based on three possible options, i.e., Gumbel-Softmax differentiation, Reinforcement Learning, and modified training objectives. All alternatives are reviewed in this survey as they present the most recent approaches for generating text using adversarial-based techniques. The selected works were taken from renowned databases, such as Science Direct, IEEEXplore, Springer, Association for Computing Machinery, and arXiv, whereas each selected work has been critically analyzed and assessed to present its objective, methodology, and experimental results.