A Survey of Text Style Transfer: Applications and Ethical Implications

TL;DR

This survey reviews the development, applications, and ethical considerations of text style transfer (TST), highlighting its transition from research to practical deployment and emphasizing the importance of ethical awareness.

Contribution

It provides a comprehensive overview of TST applications, combining traditional and deep learning approaches, and discusses challenges, future directions, and ethical issues.

Findings

TST technologies are approaching deployment readiness.

Applications span politeness, formality, sentiment control.

Ethical considerations are increasingly important.

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.