Bing Li, Peng Yang, Yuankang Sun, Zhongjian Hu, Meng Yi
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引用次数: 0
Abstract
Text generation is an essential research area in artificial intelligence (AI) technology and natural language processing and provides key technical support for the rapid development of AI-generated content (AIGC). It is based on technologies such as natural language processing, machine learning, and deep learning, which enable learning language rules through training models to automatically generate text that meets grammatical and semantic requirements. In this paper, we sort and systematically summarize the main research progress in text generation and review recent text generation papers, focusing on presenting a detailed understanding of the technical models. In addition, several typical text generation application systems are presented. Finally, we address some challenges and future directions in AI text generation. We conclude that improving the quality, quantity, interactivity, and adaptability of generated text can help fundamentally advance AI text generation development.
期刊介绍：
Frontiers of Information Technology & Electronic Engineering (ISSN 2095-9184, monthly), formerly known as Journal of Zhejiang University SCIENCE C (Computers & Electronics) (2010-2014), is an international peer-reviewed journal launched by Chinese Academy of Engineering (CAE) and Zhejiang University, co-published by Springer & Zhejiang University Press. FITEE is aimed to publish the latest implementation of applications, principles, and algorithms in the broad area of Electrical and Electronic Engineering, including but not limited to Computer Science, Information Sciences, Control, Automation, Telecommunications. There are different types of articles for your choice, including research articles, review articles, science letters, perspective, new technical notes and methods, etc.
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