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A Survey of Text Style Transfer: Applications and Ethical Implications

Sourabrata Mukherjee, Mateusz Lango, Zdenek Kasner, Ondrej Dušek

TL;DR

This survey addresses Text Style Transfer (TST) with a focus on applications and ethical implications, complementing prior method-centric reviews. It categorizes TST approaches into parallel, non-parallel, unsupervised, and large language model-based paradigms, and surveys standard evaluation criteria (style accuracy, content preservation, fluency). The paper catalogs a wide range of applications—privacy/bias correction, authorship concealing, personalized content, dialogue systems, and cross-task NLG—while highlighting open problems in evaluation, multilinguality, and multi-attribute transfer. It also discusses future directions, such as document-level TST, domain-agnostic representations, and sentiment-transfer potential, alongside ethical considerations like privacy and misuse risks. Overall, the work aims to guide researchers and practitioners toward robust, responsible deployment of TST technologies in real-world settings.

Abstract

Text style transfer (TST) is an important task in controllable text generation, which aims to control selected attributes of language use, such as politeness, formality, or sentiment, without altering the style-independent content of the text. The field has received considerable research attention in recent years and has already been covered in several reviews, but the focus has mostly been on the development of new algorithms and learning from different types of data (supervised, unsupervised, out-of-domain, etc.) and not so much on the application side. However, TST-related technologies are gradually reaching a production- and deployment-ready level, and therefore, the inclusion of the application perspective in TST research becomes crucial. Similarly, the often overlooked ethical considerations of TST technology have become a pressing issue. This paper presents a comprehensive review of TST applications that have been researched over the years, using both traditional linguistic approaches and more recent deep learning methods. We discuss current challenges, future research directions, and ethical implications of TST applications in text generation. By providing a holistic overview of the landscape of TST applications, we hope to stimulate further research and contribute to a better understanding of the potential as well as ethical considerations associated with TST.

A Survey of Text Style Transfer: Applications and Ethical Implications

TL;DR

This survey addresses Text Style Transfer (TST) with a focus on applications and ethical implications, complementing prior method-centric reviews. It categorizes TST approaches into parallel, non-parallel, unsupervised, and large language model-based paradigms, and surveys standard evaluation criteria (style accuracy, content preservation, fluency). The paper catalogs a wide range of applications—privacy/bias correction, authorship concealing, personalized content, dialogue systems, and cross-task NLG—while highlighting open problems in evaluation, multilinguality, and multi-attribute transfer. It also discusses future directions, such as document-level TST, domain-agnostic representations, and sentiment-transfer potential, alongside ethical considerations like privacy and misuse risks. Overall, the work aims to guide researchers and practitioners toward robust, responsible deployment of TST technologies in real-world settings.

Abstract

Text style transfer (TST) is an important task in controllable text generation, which aims to control selected attributes of language use, such as politeness, formality, or sentiment, without altering the style-independent content of the text. The field has received considerable research attention in recent years and has already been covered in several reviews, but the focus has mostly been on the development of new algorithms and learning from different types of data (supervised, unsupervised, out-of-domain, etc.) and not so much on the application side. However, TST-related technologies are gradually reaching a production- and deployment-ready level, and therefore, the inclusion of the application perspective in TST research becomes crucial. Similarly, the often overlooked ethical considerations of TST technology have become a pressing issue. This paper presents a comprehensive review of TST applications that have been researched over the years, using both traditional linguistic approaches and more recent deep learning methods. We discuss current challenges, future research directions, and ethical implications of TST applications in text generation. By providing a holistic overview of the landscape of TST applications, we hope to stimulate further research and contribute to a better understanding of the potential as well as ethical considerations associated with TST.
Paper Structure (40 sections, 1 figure, 1 table)

This paper contains 40 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. The Task of Text Style Transfer
  3. Parallel Supervised Approaches
  4. Non-parallel Supervised Approaches
  5. Unsupervised Approaches
  6. Using Large Language Models (LLMs)
  7. TST Evaluation
  8. Applications
  9. User Privacy and Security
  10. Bias Correction
  11. Fighting Offensive Language
  12. Concealing Authorship
  13. Creating Personalized Texts
  14. Marketing and Advertisement
  15. Text Simplification
  16. ...and 25 more sections

Figures (1)

  • Figure 1: Several examples of text style transfer: transferring sentiment, politeness and formality.