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.

翻译：文本风格迁移（TST）是可控文本生成领域的一项重要任务，其目标是在不改变文本风格无关内容的前提下，控制语言使用的特定属性（如礼貌程度、正式性或情感倾向）。近年来该领域受到广泛研究关注，已有若干综述文献，但其焦点多集中于新算法的开发及从不同类型数据（有监督、无监督、跨领域等）中学习的方法，对应用层面的探讨相对不足。然而，TST相关技术正逐步达到可投入生产部署的成熟度，因此在TST研究中纳入应用视角变得至关重要。同样，长期被忽视的TST技术伦理考量已成为亟待解决的问题。本文系统综述了多年来采用传统语言学方法及近期深度学习方法所研究的TST应用，探讨当前挑战、未来研究方向以及TST在文本生成应用中的伦理影响。通过对TST应用领域进行全面梳理，我们希望推动进一步研究，并促进学界更深入理解TST的潜力及其相关的伦理考量。