---
title: "Style Transfer in Text: Exploration and Evaluation — Fu 2017"
description: "The paper addresses text style transfer when no aligned (parallel) sentence pairs exist and when standard metrics like BLEU have no gold references to…"
authors: ["Zhenxin Fu", "Xiaoye Tan", "Nanyun Peng", "Dongyan Zhao", "Rui Yan"]
year: 2017
venue: "arXiv:1711.06861"
source: "https://arxiv.org/abs/1711.06861"
doi: "10.48550/arxiv.1711.06861"
arxiv: "1711.06861"
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/papers/fu-2017-style-transfer-text-exploration-evaluation/"
---
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# Style Transfer in Text: Exploration and Evaluation

**Authors:** Zhenxin Fu, Xiaoye Tan, [Nanyun Peng](https://md.research.elamur.ai/free/authors/nanyun-peng/), Dongyan Zhao, [Rui Yan](https://md.research.elamur.ai/free/authors/rui-yan/) · **Year:** 2017 · **Venue:** arXiv:1711.06861 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1711.06861) · [PDF](https://arxiv.org/pdf/1711.06861) · [DOI 10.48550/arxiv.1711.06861](https://doi.org/10.48550/arxiv.1711.06861) · [arXiv 1711.06861](https://arxiv.org/abs/1711.06861) · [OpenAlex](https://openalex.org/W2964008635)

**Citations:** 460 citations (openalex-snapshot · 2026-10-14) · [241](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses text style transfer when no aligned (parallel) sentence pairs exist and when standard metrics like BLEU have no gold references to compare against. The authors build on an auto-encoder seq2seq model with a GRU encoder and add an adversarial style classifier (an MLP followed by softmax). The adversary pushes the encoder's output to carry content but no style signal. On this shared encoder they test two generators. The multi-decoder model uses one decoder per style. The style-embedding model has a single decoder that receives the content vector concatenated with a learned style vector. Evaluation uses two separate axes: transfer strength, measured with a style classifier, and content preservation. The second is shown to correlate highly with human judgments. Experiments cover two tasks: paper-to-news title conversion, using a newly released dataset, and positive/negative review transfer. Both models beat a plain auto-encoder on transfer strength while staying well above a random-pair floor on content preservation. The work matters because it frames how to train and judge style transfer without parallel corpora.

## Key points
- The two proposed architectures are **multi-decoder** and **style-embedding**. Both are seq2seq models with GRU units and an adversarial classifier on the content representation **c**.
- The authors release a paper-news title dataset at github.com/fuzhenxin/textstyletransferdata. A positive-negative review dataset is also used.
- On paper-news titles, the auto-encoder baseline shows almost zero transfer strength. On reviews, the classifier behind transfer strength reaches only 84.8% validation accuracy, so that score is less reliable there.
- A lower bound for content preservation was estimated from 2,000 randomly sampled sentence pairs: **0.609** for paper-news and **0.863** for reviews. Both models score clearly above these floors.
- The two models behave differently by task:
  - Multi-decoder tends to give strong style change but weaker content retention.
  - Style-embedding spans a broad range of trade-offs.
  - On reviews, multi-decoder outperforms style-embedding on both metrics.

## Interesting ideas
- **Adversarially "scrubbing" a representation.** A discriminator tries to predict a nuisance attribute from the encoding, and the encoder learns to defeat it. This trick could remove other unwanted signals, such as topic or regional spelling variants, from shared representations in NLP pipelines.
- **Two-axis evaluation instead of one score.** Style change and meaning retention are reported separately and plotted as a trade-off. This shows that an identity-copying model can look good on meaning while failing the actual task, and it fits any rewriting task (simplification, normalization).
- **A random-pair floor for similarity metrics.** Scoring randomly paired sentences gives a baseline that reveals how much of a similarity score is "free." This is a cheap sanity check whenever a reference-free metric is introduced for a new corpus.
- **Parameter sharing as a control knob.** The paper reads auto-encoder, style-embedding and multi-decoder as points on a multi-task sharing spectrum. Sharing more parameters keeps more content and needs less data, while separate decoders give stronger style shifts. This is a useful heuristic when choosing architectures under limited data.
- **Validating the evaluator itself.** Checking the style classifier's own accuracy before trusting transfer-strength numbers catches cases where the metric is noisy. Here, some reviews carry little sentiment.

*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/shelves/machine-translation/)

## Cited by

Cited by

241 papers of our library cite it (of the 140673 whose bibliographies we read); 22 of them have a page here, 219 not yet.

- [Evaluating prose style transfer with the Bible](https://md.research.elamur.ai/free/papers/carlson-2017-evaluating-prose-style-transfer-bible/)
- [R^3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge](https://md.research.elamur.ai/free/papers/chakrabarty-2020-r-textasciicircum3-reverse-retrieve-rank/)
- [Controllable Paraphrase Generation with a Syntactic Exemplar](https://md.research.elamur.ai/free/papers/chen-2019-controllable-paraphrase-generation-syntactic-exemplar/)
- [Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation](https://md.research.elamur.ai/free/papers/dai-2019-style-transformer-unpaired-text-style/)
- [Acquiring a Formality-Informed Lexical Resource for Style Analysis](https://md.research.elamur.ai/free/papers/eder-2021-acquiring-formality-informed-lexical-resource/)
- [StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements](https://md.research.elamur.ai/free/papers/fisher-2024-styleremix-interpretable-authorship-obfuscation-distillation/)
- [Facilitating the Communication of Politeness through Fine-Grained Paraphrasing](https://md.research.elamur.ai/free/papers/fu-2020-facilitating-communication-politeness-through-fine/)
- [T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation](https://md.research.elamur.ai/free/papers/jangra-2022-t-star-truthful-style-transfer/)
- [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://md.research.elamur.ai/free/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://md.research.elamur.ai/free/papers/li-2018-delete-retrieve-generate-simple-approach/)
- [ParaDetox: Detoxification with Parallel Data](https://md.research.elamur.ai/free/papers/logacheva-2022-paradetox-detoxification-parallel-data/)
- [A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer](https://md.research.elamur.ai/free/papers/luo-2019-dual-reinforcement-learning-framework-unsupervised/)
- [Diff4TST: Masked Diffusion Language Model for Text Style Transfer](https://md.research.elamur.ai/free/papers/ma-2026-diff4tst-masked-diffusion-language-model/)
- [Politeness Transfer: A Tag and Generate Approach](https://md.research.elamur.ai/free/papers/madaan-2020-politeness-transfer-tag-generate-approach/)
- [Style Transfer Through Back-Translation](https://md.research.elamur.ai/free/papers/prabhumoye-2018-style-transfer-through-back-translation/)
- [Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer](https://md.research.elamur.ai/free/papers/qi-2021-mind-style-text-adversarial-backdoor/)
- [A Recipe For Arbitrary Text Style Transfer with Large Language Models](https://md.research.elamur.ai/free/papers/reif-2021-recipe-arbitrary-text-style-transfer/)
- [Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer](https://md.research.elamur.ai/free/papers/santos-2018-fighting-offensive-language-social-media/)
- [“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer](https://md.research.elamur.ai/free/papers/sudhakar-2019-transforming-delete-retrieve-generate-approach/)
- [Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach](https://md.research.elamur.ai/free/papers/xu-2018-unpaired-sentiment-sentiment-translation-cycled/)
- [Unsupervised Text Style Transfer using Language Models as Discriminators](https://md.research.elamur.ai/free/papers/yang-2018-unsupervised-text-style-transfer-using/)
- [Style Transfer as Unsupervised Machine Translation](https://md.research.elamur.ai/free/papers/zhang-2018-style-transfer-unsupervised-machine-translation/)

## Cite

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

```bibtex
@misc{fu2017style,
  title = {{Style Transfer in Text: Exploration and Evaluation}},
  author = {Fu, Zhenxin and Tan, Xiaoye and Peng, Nanyun and Zhao, Dongyan and Yan, Rui},
  year = {2017},
  doi = {10.48550/arxiv.1711.06861},
  eprint = {1711.06861},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1711.06861}
}
```

- [← previous Style Transfer from Non-Parallel Text by Cross-Alignment](https://md.research.elamur.ai/free/papers/shen-2017-style-transfer-non-parallel-text/)
- [next → Stylometry in Computer-Assisted Translation: Experiments on the Babylonian Talmud](https://md.research.elamur.ai/free/papers/aclbulk-2017-giovannetti-stylometry-computer-assisted-translation-experiments/)
- [↑ back to papers of 2017](https://md.research.elamur.ai/free/papers/2017/page/3/)

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