mStyleDistance: Multilingual Style Embeddings and their Evaluation

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

mStyleDistance: Multilingual Style Embeddings and their Evaluation: 2 upvotes on Hugging Face Daily Papers, #25 of 34 papers on 2025-02-24. Day-by-day upvote history.

Style embeddings are useful for stylistic analysis and style transfer; however, only English style embeddings have been made available. We introduce Multilingual StyleDistance (mStyleDistance), a multilingual style embedding model trained using synthetic data and contrastive learning. We train the model on data from nine languages and create a multilingual STEL-or-Content benchmark (Wegmann et al., 2022) that serves to assess the embeddings' quality. We also employ our embeddings in an authorship verification task involving different languages. Our results show that mStyleDistance embeddings outperform existing models on these multilingual style benchmarks and generalize well to unseen features and languages. We make our model publicly available at https://huggingface.co/StyleDistance/mstyledistance .

Paper page on Hugging Face · arXiv

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