Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

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The Principles and Functions of Various Models for Text Style Transformation

Authors
Haolun He1, *
1School of computing, Beijing University of Technology, Beijing, China
*Corresponding author. Email: hehaolun@emails.bjut.edu.cn
Corresponding Author
Haolun He
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_39How to use a DOI?
Keywords
Text Style Transfer; Supervised Learning; Semi-supervised Learning; Unsupervised Learning
Abstract

Text style transfer is a significant research task in natural language processing, with its core objective being to transform the style of a text while maintaining the original semantic content. This paper reviews and summarizes existing research from the perspectives of supervised learning, semi-supervised learning, and unsupervised learning. Firstly, it introduces the supervised methods based on parallel corpora and their advantages and disadvantages. Secondly, it analyzes the semi-supervised methods that combine limited parallel data with large-scale non-parallel data. Finally, it focuses on the unsupervised methods that rely solely on non-parallel corpora and their latest progress. This paper also summarizes the commonly used datasets and evaluation metrics, points out the shortcomings of existing automated evaluations, and discusses the dual impact of text style transfer on information dissemination, human-computer interaction, and social applications. In conclusion, the development of text style transfer not only depends on algorithm innovation but also requires consideration of data construction, evaluation systems, and ethical norms, which will play an important foundation for future research and applications.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_39How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Haolun He
PY  - 2026
DA  - 2026/04/24
TI  - The Principles and Functions of Various Models for Text Style Transformation
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 355
EP  - 361
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_39
DO  - 10.2991/978-94-6239-648-7_39
ID  - He2026
ER  -