Recent advances of neural text generation: Core tasks, datasets, models and challenges

In recent years, deep neural network has achieved great success in solving many natural language processing tasks. Particularly, substantial progress has been made on neural text generation, which takes the linguistic and non-linguistic input, and generates natural language text. This survey aims to...

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Bibliographic Details
Published in:Science China. Technological sciences 2020-10, Vol.63 (10), p.1990-2010
Main Authors: Jin, HanQi, Cao, Yue, Wang, TianMing, Xing, XinYu, Wan, XiaoJun
Format: Article
Language:English
Subjects:
Artificial neural networks
Datasets
Engineering
Natural language
Natural language processing
Review
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Summary:In recent years, deep neural network has achieved great success in solving many natural language processing tasks. Particularly, substantial progress has been made on neural text generation, which takes the linguistic and non-linguistic input, and generates natural language text. This survey aims to provide an up-to-date synthesis of core tasks in neural text generation and the architectures adopted to handle these tasks, and draw attention to the challenges in neural text generation. We first outline the mainstream neural text generation frameworks, and then introduce datasets, advanced models and challenges of four core text generation tasks in detail, including AMR-to-text generation, data-to-text generation, and two text-to-text generation tasks (i.e., text summarization and paraphrase generation). Finally, we present future research directions for neural text generation. This survey can be used as a guide and reference for researchers and practitioners in this area.
ISSN:1674-7321
1869-1900
DOI:10.1007/s11431-020-1622-y