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

Weiqi Wang, Dan Jiang () and Shaozhong Cao ()
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Weiqi Wang: Beijing Institute of Graphic Communication
Dan Jiang: Beijing Institute of Graphic Communication
Shaozhong Cao: Beijing Institute of Graphic Communication

A chapter in IEIS 2022, 2023, pp 109-122 from Springer

Abstract: Abstract In recent years, in the field of natural language processing, significant progress has been made in text generation. Text generation has gained widespread popularity in many fields such as abstract extraction, poetry creation, and response to social network comments. Given the excellent generative capabilities of Generative Adversarial Networks (GAN), it is often used as main model for text generation with remarkable results. This review aims to provide the core tasks of generative adversarial network text generation and the architecture used to deal with these tasks, and draw attention to the challenges in text generation with generative adversarial network. Firstly, we outline the mainstream text generation models, and then introduce datasets, advanced models and challenges of text generation tasks in detail. Finally, we discuss the prospects and challenges of the fusion of generative adversarial networks and text generation tasks in the future.

Keywords: Deep learning; text generation; natural language processing; generative adversarial networks (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-99-3618-2_11

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DOI: 10.1007/978-981-99-3618-2_11

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