mlsanityData to 7 Oct 2026

Towards Universality in Multilingual Text Rewriting

arXiv 2107.14749 ↗First posted 30 Jul 2021cs.CLcited by 7

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Abstract (arXiv)

In this work, we take the first steps towards building a universal rewriter: a model capable of rewriting text in any language to exhibit a wide variety of attributes, including styles and languages, while preserving as much of the original semantics as possible. In addition to obtaining state-of-the-art results on unsupervised translation, we also demonstrate the ability to do zero-shot sentiment transfer in non-English languages using only English exemplars for sentiment. We then show that our model is able to modify multiple attributes at once, for example adjusting both language and sentiment jointly. Finally, we show that our model is capable of performing zero-shot formality-sensitive translation.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 25 of its 39 references that are in the map. First posted on arXiv 30 Jul 2021.

Builds on

25 of 39 references.

References · 25

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13 more
  1. 2020Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translationcited by 51
  2. 2021XFORMAL: A Benchmark for Multilingual Formality Style Transfercited by 51
  3. 2020CoCon: A Self-Supervised Approach for Controlled Text Generationcited by 47
  4. 2020When and Why is Unsupervised Neural Machine Translation Useless?cited by 34
  5. 2018Multi-Task Neural Models for Translating Between Styles Within and Across Languagescited by 28
  6. 2018Extreme Adaptation for Personalized Neural Machine Translationcited by 27
  7. 2020A Multilingual View of Unsupervised Machine Translationcited by 23
  8. 2020Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languagescited by 22
  9. 2021Machine Translation into Low-resource Language Varietiescited by 19
  10. 2020Reference Language based Unsupervised Neural Machine Translationcited by 12
  11. 2018Neural Machine Translation into Language Varietiescited by 9
  12. 2021Towards Personalised and Document-level Machine Translation of Dialoguecited by 4
  13. 2018Unsupervised Learning of Style-sensitive Word Vectorscited by 2

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 1; 2025: 1.

7 papers.

  1. 2023The unreasonable effectiveness of few-shot learning for machine translationcited by 91
  2. 2021Few-shot Controllable Style Transfer for Low-Resource Multilingual Settingscited by 20
  3. 2022FRMT: A Benchmark for Few-Shot Region-Aware Machine Translationcited by 18
  4. 2023RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translationcited by 9
  5. 2025Towards Style Alignment in Cross-Cultural Translationcited by 4
  6. 2023Comparing Styles across Languages: A Cross-Cultural Exploration of Politenesscited by 2
  7. 2024A Survey of Text Style Transfer: Applications and Ethical Implicationscited by 0