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Recent Advances in Neural Text Generation: A Task-Agnostic Survey

2022-03-06 20:47:49
Chen Tang, Frank Guerin, Yucheng Li, Chenghua Lin
arXiv_AI
         
Abstract

In recent years much effort has been devoted to applying neural models to the task of natural language generation. The challenge is to generate natural human-like text, and to control the generation process. This paper presents a task-agnostic survey of recent advances in neural text generation. These advances have been achieved by numerous developments, which we group under the following four headings: data construction, neural frameworks, training and inference strategies, and evaluation metrics. Finally we discuss the future directions for the development of neural text generation including neural pipelines and exploiting back-ground knowledge.

Abstract (translated)

URL

https://arxiv.org/abs/2203.03047

PDF

https://arxiv.org/pdf/2203.03047.pdf

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