# Text Style Transfer: A Review and Experimental Evaluation

arXiv: https://arxiv.org/abs/2010.12742 · HTML: https://mlsanity.com/paper/2010.12742/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation](https://mlsanity.com/paper/2210.00193/) (2022)
- [VERVE: Template-based ReflectiVE Rewriting for MotiVational IntErviewing](https://mlsanity.com/paper/2311.08299/) (2023)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Visual Captioning at Will: Describing Images and Videos Guided by a Few Stylized Sentences](https://mlsanity.com/paper/2307.16399/) (2023)
- [Syntax Matters! Syntax-Controlled in Text Style Transfer](https://mlsanity.com/paper/2108.05869/) (2021)
- [Language Invariant Properties in Natural Language Processing](https://mlsanity.com/paper/2109.13037/) (2021)
- [Fine-Tuning Language Models for Scientific Writing Support](https://mlsanity.com/paper/2306.10974/) (2023)
- [A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception](https://mlsanity.com/paper/2208.05757/) (2022)
- [On Text Style Transfer via Style Masked Language Models](https://mlsanity.com/paper/2210.06394/) (2022)

## Abstract (arXiv)

The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the Text Style Transfer (TST) task, which aims to change the stylistic properties of the text while retaining its style independent content. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. The field of TST research has burgeoned because of this symbiosis. This article aims to provide a comprehensive review of recent research efforts on text style transfer. More concretely, we create a taxonomy to organize the TST models and provide a comprehensive summary of the state of the art. We review the existing evaluation methodologies for TST tasks and conduct a large-scale reproducibility study where we experimentally benchmark 19 state-of-the-art TST algorithms on two publicly available datasets. Finally, we expand on current trends and provide new perspectives on the new and exciting developments in the TST field.