Text Style Transfer: An Introductory Overview

TL;DR

This paper provides an introductory overview of Text Style Transfer, discussing its challenges, approaches, datasets, evaluation, and applications to enhance understanding of this key natural language generation task.

Contribution

It offers a comprehensive summary of TST fundamentals, addressing its challenges, approaches, datasets, and evaluation methods, serving as a foundational overview.

Findings

TST involves manipulating style attributes while preserving content.

Recent advancements have expanded TST applications and datasets.

Evaluation measures for TST are evolving to better assess quality.

Abstract

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.