# Style Transfer as Unsupervised Machine Translation

arXiv: https://arxiv.org/abs/1808.07894 · HTML: https://mlsanity.com/paper/1808.07894/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2018.

## Built on by (most cited first, 19 of 19)

- [Break-It-Fix-It: Unsupervised Learning for Program Repair](https://mlsanity.com/paper/2106.06600/) (2021)
- [Civil Rephrases Of Toxic Texts With Self-Supervised Transformers](https://mlsanity.com/paper/2102.05456/) (2021)
- [Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances](https://mlsanity.com/paper/2204.10825/) (2022)
- [Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer](https://mlsanity.com/paper/2105.06947/) (2021)
- [Text Style Transfer for Bias Mitigation using Masked Language Modeling](https://mlsanity.com/paper/2201.08643/) (2022)
- [Why is constrained neural language generation particularly challenging?](https://mlsanity.com/paper/2206.05395/) (2022)
- [Text Revision by On-the-Fly Representation Optimization](https://mlsanity.com/paper/2204.07359/) (2022)
- [Semi-Supervised Formality Style Transfer with Consistency Training](https://mlsanity.com/paper/2203.13620/) (2022)
- [Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2205.11116/) (2022)
- [Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs](https://mlsanity.com/paper/2406.04460/) (2024)
- [Text Style Transfer: An Introductory Overview](https://mlsanity.com/paper/2407.14822/) (2024)
- [Conversation Style Transfer using Few-Shot Learning](https://mlsanity.com/paper/2302.08362/) (2023)
- [GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer](https://mlsanity.com/paper/2102.00769/) (2021)
- [ViPE: Visualise Pretty-much Everything](https://mlsanity.com/paper/2310.10543/) (2023)
- [Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer](https://mlsanity.com/paper/2305.05945/) (2023)
- [Multi-Pair Text Style Transfer on Unbalanced Data](https://mlsanity.com/paper/2106.10608/) (2021)
- [A Survey of Text Style Transfer: Applications and Ethical Implications](https://mlsanity.com/paper/2407.16737/) (2024)
- [From Detection to Mitigation: Addressing Gender Bias in Chinese Texts via Efficient Tuning and Voting-Based Rebalancing](https://mlsanity.com/paper/2509.07889/) (2025)
- [RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translation](https://mlsanity.com/paper/2602.15013/) (2026)

## Abstract (arXiv)

Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content. One main challenge in learning a style transfer system is a lack of parallel data where the source sentence is in one style and the target sentence in another style. With this constraint, in this paper, we adapt unsupervised machine translation methods for the task of automatic style transfer. We first take advantage of style-preference information and word embedding similarity to produce pseudo-parallel data with a statistical machine translation (SMT) framework. Then the iterative back-translation approach is employed to jointly train two neural machine translation (NMT) based transfer systems. To control the noise generated during joint training, a style classifier is introduced to guarantee the accuracy of style transfer and penalize bad candidates in the generated pseudo data. Experiments on benchmark datasets show that our proposed method outperforms previous state-of-the-art models in terms of both accuracy of style transfer and quality of input-output correspondence.