Text Style Transfer: An Introductory Overview
It offers a foundational resource for researchers and practitioners in NLP by consolidating existing knowledge on TST, but it is incremental as it does not introduce new methods or results.
This paper provides an introductory overview of Text Style Transfer (TST), a task in natural language generation that manipulates text style attributes like politeness or formality while preserving content, summarizing its challenges, approaches, datasets, and applications.
Text Style Transfer (TST) is a pivotal task in natural language generation to manipulate text style attributes while preserving style-independent content. The attributes targeted in TST can vary widely, including politeness, authorship, mitigation of offensive language, modification of feelings, and adjustment of text formality. TST has become a widely researched topic with substantial advancements in recent years. This paper provides an introductory overview of TST, addressing its challenges, existing approaches, datasets, evaluation measures, subtasks, and applications. This fundamental overview improves understanding of the background and fundamentals of text style transfer.