mStyleDistance: Multilingual Style Embeddings and their Evaluation

Justin Qiu; Jiacheng Zhu; Ajay Patel; Marianna Apidianaki; Chris Callison-Burch

doi:10.18653/v1/2025.findings-acl.869

mStyleDistance: Multilingual Style Embeddings and their Evaluation

Justin Qiu, Jiacheng Zhu, Ajay Patel, Marianna Apidianaki, Chris Callison-Burch

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Abstract

Style embeddings are useful for stylistic analysis and style transfer, yet they only exist for English. We introduce Multilingual StyleDistance (mStyleDistance), a method that can generate style embeddings in new languages using synthetic data and a contrastive loss. We create style embeddings in nine languages and a multilingual STEL-or-Content benchmark (Wegmann et al., 2022) that serves to assess their quality. We also employ our embeddings in an authorship verification task involving different languages. Our results show that mStyleDistance embeddings outperform existing style embeddings on these benchmarks and generalize well to unseen features and languages. We make our models and datasets publicly available.

Anthology ID:: 2025.findings-acl.869
Volume:: Findings of the Association for Computational Linguistics: ACL 2025
Month:: July
Year:: 2025
Address:: Vienna, Austria
Editors:: Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:: Findings
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 16917–16931
Language:
URL:: https://aclanthology.org/2025.findings-acl.869/
DOI:: 10.18653/v1/2025.findings-acl.869
Bibkey:
Cite (ACL):: Justin Qiu, Jiacheng Zhu, Ajay Patel, Marianna Apidianaki, and Chris Callison-Burch. 2025. mStyleDistance: Multilingual Style Embeddings and their Evaluation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 16917–16931, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):: mStyleDistance: Multilingual Style Embeddings and their Evaluation (Qiu et al., Findings 2025)
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PDF:: https://aclanthology.org/2025.findings-acl.869.pdf

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@inproceedings{qiu-etal-2025-mstyledistance,
    title = "m{S}tyle{D}istance: Multilingual Style Embeddings and their Evaluation",
    author = "Qiu, Justin  and
      Zhu, Jiacheng  and
      Patel, Ajay  and
      Apidianaki, Marianna  and
      Callison-Burch, Chris",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.869/",
    doi = "10.18653/v1/2025.findings-acl.869",
    pages = "16917--16931",
    ISBN = "979-8-89176-256-5",
    abstract = "Style embeddings are useful for stylistic analysis and style transfer, yet they only exist for English. We introduce Multilingual StyleDistance (mStyleDistance), a method that can generate style embeddings in new languages using synthetic data and a contrastive loss. We create style embeddings in nine languages and a multilingual STEL-or-Content benchmark (Wegmann et al., 2022) that serves to assess their quality. We also employ our embeddings in an authorship verification task involving different languages. Our results show that mStyleDistance embeddings outperform existing style embeddings on these benchmarks and generalize well to unseen features and languages. We make our models and datasets publicly available."
}

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%0 Conference Proceedings
%T mStyleDistance: Multilingual Style Embeddings and their Evaluation
%A Qiu, Justin
%A Zhu, Jiacheng
%A Patel, Ajay
%A Apidianaki, Marianna
%A Callison-Burch, Chris
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F qiu-etal-2025-mstyledistance
%X Style embeddings are useful for stylistic analysis and style transfer, yet they only exist for English. We introduce Multilingual StyleDistance (mStyleDistance), a method that can generate style embeddings in new languages using synthetic data and a contrastive loss. We create style embeddings in nine languages and a multilingual STEL-or-Content benchmark (Wegmann et al., 2022) that serves to assess their quality. We also employ our embeddings in an authorship verification task involving different languages. Our results show that mStyleDistance embeddings outperform existing style embeddings on these benchmarks and generalize well to unseen features and languages. We make our models and datasets publicly available.
%R 10.18653/v1/2025.findings-acl.869
%U https://aclanthology.org/2025.findings-acl.869/
%U https://doi.org/10.18653/v1/2025.findings-acl.869
%P 16917-16931

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Markdown (Informal)

[mStyleDistance: Multilingual Style Embeddings and their Evaluation](https://aclanthology.org/2025.findings-acl.869/) (Qiu et al., Findings 2025)

mStyleDistance: Multilingual Style Embeddings and their Evaluation (Qiu et al., Findings 2025)

ACL

Justin Qiu, Jiacheng Zhu, Ajay Patel, Marianna Apidianaki, and Chris Callison-Burch. 2025. mStyleDistance: Multilingual Style Embeddings and their Evaluation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 16917–16931, Vienna, Austria. Association for Computational Linguistics.