# Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites

arXiv: https://arxiv.org/abs/1908.06809 · HTML: https://mlsanity.com/paper/1908.06809/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 20 of 20)

- [RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting](https://mlsanity.com/paper/2305.15685/) (2023)
- [XFORMAL: A Benchmark for Multilingual Formality Style Transfer](https://mlsanity.com/paper/2104.04108/) (2021)
- [A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations](https://mlsanity.com/paper/2105.02685/) (2021)
- [Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer](https://mlsanity.com/paper/2110.10668/) (2021)
- [Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings](https://mlsanity.com/paper/2110.07385/) (2021)
- [A Call for Standardization and Validation of Text Style Transfer Evaluation](https://mlsanity.com/paper/2306.00539/) (2023)
- [Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer](https://mlsanity.com/paper/2204.07549/) (2022)
- [Towards an On-device Agent for Text Rewriting](https://mlsanity.com/paper/2308.11807/) (2023)
- [Evaluating Text Style Transfer Evaluation: Are There Any Reliable Metrics?](https://mlsanity.com/paper/2502.04718/) (2025)
- [T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation](https://mlsanity.com/paper/2212.01667/) (2022)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [DYPLODOC: Dynamic Plots for Document Classification](https://mlsanity.com/paper/2107.12226/) (2021)
- [Rethinking Crowd Sourcing for Semantic Similarity](https://mlsanity.com/paper/2109.11969/) (2021)
- [Exploring Code Style Transfer with Neural Networks](https://mlsanity.com/paper/2209.06273/) (2022)
- [SE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2104.12977/) (2021)
- [Out of style: Misadventures with LLMs and code style transfer](https://mlsanity.com/paper/2406.10320/) (2024)
- [Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style](https://mlsanity.com/paper/2604.24444/) (2026)
- [Empirical Evaluation of Supervision Signals for Style Transfer Models](https://mlsanity.com/paper/2101.06172/) (2021)
- [SimpleStyle: An Adaptable Style Transfer Approach](https://mlsanity.com/paper/2212.10498/) (2022)
- [Metric-Construction Coupling Inflates Measured Synthetic Dialect Recovery](https://mlsanity.com/paper/2610.00164/) (2026)

## Abstract (arXiv)

This paper shows that standard assessment methodology for style transfer has several significant problems. First, the standard metrics for style accuracy and semantics preservation vary significantly on different re-runs. Therefore one has to report error margins for the obtained results. Second, starting with certain values of bilingual evaluation understudy (BLEU) between input and output and accuracy of the sentiment transfer the optimization of these two standard metrics diverge from the intuitive goal of the style transfer task. Finally, due to the nature of the task itself, there is a specific dependence between these two metrics that could be easily manipulated. Under these circumstances, we suggest taking BLEU between input and human-written reformulations into consideration for benchmarks. We also propose three new architectures that outperform state of the art in terms of this metric.