@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://preview.aclanthology.org/ingestion-acl-25/2025.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."
}