A review of unsupervised text style transfer based on deep learning

Zicheng Guo; Yuan Rao

doi:10.1117/12.2684814

19 October 2023 A review of unsupervised text style transfer based on deep learning

Zicheng Guo, Yuan Rao

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127091E (2023) https://doi.org/10.1117/12.2684814
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Text style transfer is mainly to modify the text style to suit various application scenarios without changing the semantic meaning of the text, which is a great significant issue in natural language processing. To expedite research progress, this survey conducts a systematic review of existing literature. Drawing from a vast body of research, this survey first extracts the essential connotations of style information in text of varying granularity across different tasks, and then provide a clear definition of text style transfer, summarize the main challenges at present and comprehensively codify and discuss the current datasets used for evaluation, as well as their indicators. Moreover, this survey compares the mechanisms, advantages, and shortcomings of unsupervised classical methods across word, sentence, and paragraph levels. Finally, the future directions of fighting are also given, hoping to facilitate more comprehensive solutions.

Citation Download Citation

Zicheng Guo and Yuan Rao "A review of unsupervised text style transfer based on deep learning", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127091E (19 October 2023); https://doi.org/10.1117/12.2684814

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KEYWORDS

Data modeling

Education and training

Deep learning

Statistical modeling

Performance modeling

Reverse modeling

Lithium

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