---
title: "Unsupervised Text Style Transfer using Language — Yang 2018"
description: "Unsupervised text style transfer rewrites a sentence so that it carries a different attribute, such as sentiment, cipher orthography or a closely related…"
authors: ["Zichao Yang", "Zhiting Hu", "Chris Dyer", "Eric P. Xing", "Taylor Berg-Kirkpatrick"]
year: 2018
venue: "arXiv:1805.11749"
source: "https://arxiv.org/abs/1805.11749"
doi: "10.48550/arxiv.1805.11749"
arxiv: "1805.11749"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yang-2018-unsupervised-text-style-transfer-using/"
---
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# Unsupervised Text Style Transfer using Language Models as Discriminators

**Authors:** [Zichao Yang](https://md.research.elamur.ai/free/en/authors/zichao-yang/), [Zhiting Hu](https://md.research.elamur.ai/free/en/authors/zhiting-hu/), [Chris Dyer](https://md.research.elamur.ai/free/en/authors/chris-dyer/), [Eric P. Xing](https://md.research.elamur.ai/free/en/authors/eric-p-xing/), [Taylor Berg-Kirkpatrick](https://md.research.elamur.ai/free/en/authors/taylor-berg-kirkpatrick/) · **Year:** 2018 · **Venue:** arXiv:1805.11749 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1805.11749) · [PDF](https://arxiv.org/pdf/1805.11749) · [DOI 10.48550/arxiv.1805.11749](https://doi.org/10.48550/arxiv.1805.11749) · [arXiv 1805.11749](https://arxiv.org/abs/1805.11749) · [OpenAlex](https://openalex.org/W2805041018)

**Citations:** 43 citations (openalex-snapshot · 2026-10-14) · [113](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Unsupervised text style transfer rewrites a sentence so that it carries a different attribute, such as sentiment, cipher orthography or a closely related language, while its content stays the same. The model must learn this without paired examples. Earlier encoder-decoder systems judged the outputs with binary classifiers, either style classifiers or real/fake CNN discriminators. That feedback is coarse, can be unstable under adversarial training, and often fails to produce fluent text.

The authors replace the classifier with a language model trained on target-domain sentences. The generator is optimized to lower the NLL that this LM assigns to its outputs, which yields a signal for every token rather than one score per sentence. A continuous approximation of discrete sampling keeps the pipeline differentiable, so everything trains end to end with back-propagation. Because a normalized LM already gives low probability to out-of-domain text, the authors find that negative samples and the adversarial step can be dropped, which makes training steadier.

Across three tasks (cracking word-substitution ciphers, changing the sentiment of a sentence, and translating between closely related languages), the method outperforms CNN-discriminator baselines such as Shen et al. (2017). This makes it a practical way to inject monolingual target-domain knowledge into a generator.

## Key points
- **Training objective.** The model combines an auto-encoder reconstruction loss with LM-based discriminator losses for each direction (LM_x, LM_y). A weight γ on the negative-sample term lets the authors test whether adversarial negatives are needed at all.
- **Serbian–Bosnian (sr/bs) data.** About 200k news sentences per language, each shorter than 20 tokens, come from the Leipzig Corpora Collection. The split is 80/10/10, the vocabulary is 25k, and reference translations for validation and test were created with the Google Translation API.
- **Simplified–Traditional Chinese (zh-CN/zh-TW) data.** 300k news headlines per variant come from the Chinese Gigaword corpus. The model is character-based with a vocabulary of roughly 5k.
- **Serbian–Bosnian results, in BLEU1** (BLEU4 was near zero because the vocabularies do not overlap):

  | Direction | LM discriminator | Shen et al. |
  |---|---|---|
  | sr–bs | 31.0 | 29.1 |
  | bs–sr | 31.7 | 30.3 |

- **Chinese results, in BLEU.** Simply copying the input already scores 32.3.

  | Direction | LM discriminator | Shen et al. |
  |---|---|---|
  | cn–tw | 81.6 | 60.1 |
  | tw–cn | 85.5 | 60.7 |

## Interesting ideas
- **Use a target-domain LM as the critic.** A language model scores each token, so it shows which words make an output sound off-domain. A sentence-level classifier cannot do this. The same feedback could help any generator that must sound native to a particular domain or language variety.
- **Rely on normalization instead of negative samples.** A locally normalized LM already penalizes text that does not look like its training data. This makes it possible to skip the unstable min-max game, a useful lesson for anyone who finds GAN-style training on text fragile.
- **Make sampling differentiable.** A continuous relaxation of token sampling lets gradients from the LM flow straight into the generator. This avoids REINFORCE-style fine-tuning and could carry over to other discrete-output tasks with a differentiable scorer.
- **Build cheap evaluation sets for related languages.** The authors collected monolingual news, filtered it by sentence length, and created parallel validation and test sets with machine translation. They also reported a plain copy baseline as a floor. This setup could be reused to benchmark conversion between close language varieties when no parallel corpus exists.
- **Match the granularity to the task.** For script conversion such as zh-CN/zh-TW, which behaves almost like decipherment, a character-level model with a small vocabulary worked well. Choosing the unit to fit the nature of the mapping is a simple design choice worth copying.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/)

## Cited by

Cited by

113 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 107 not yet.

- [Acquiring a Formality-Informed Lexical Resource for Style Analysis](https://md.research.elamur.ai/free/en/papers/eder-2021-acquiring-formality-informed-lexical-resource/)
- [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
- [Syntax-Guided Controlled Generation of Paraphrases](https://md.research.elamur.ai/free/en/papers/kumar-2020-syntax-guided-controlled-generation-paraphrases/)
- [A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer](https://md.research.elamur.ai/free/en/papers/luo-2019-dual-reinforcement-learning-framework-unsupervised/)
- [Politeness Transfer: A Tag and Generate Approach](https://md.research.elamur.ai/free/en/papers/madaan-2020-politeness-transfer-tag-generate-approach/)
- [Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer](https://md.research.elamur.ai/free/en/papers/qi-2021-mind-style-text-adversarial-backdoor/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{yang2018unsupervised,
  title = {{Unsupervised Text Style Transfer using Language Models as Discriminators}},
  author = {Yang, Zichao and Hu, Zhiting and Dyer, Chris and Xing, Eric P. and Berg-Kirkpatrick, Taylor},
  year = {2018},
  doi = {10.48550/arxiv.1805.11749},
  eprint = {1805.11749},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1805.11749}
}
```

- [← previous Unsupervised Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/artetxe-2018-unsupervised-statistical-machine-translation/)
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- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/4/)

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