mStyleDistance: Multilingual Style Embeddings and their Evaluation
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 .
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Justin Qiu
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2025-03-20T10:54:00.176Z
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Justin Q
user
justinsunqiu
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Jiacheng Zhu
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2025-03-19T09:48:00.617Z
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Jiacheng Zhu
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Ajay Patel
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2025-02-24T16:39:31.830Z
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Ajay Patel
user
AjayP13
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Marianna Apidianaki
67bc90e38915fc3c91098aa2
Chris Callison-Burch
admin_assigned
2025-02-24T16:13:29.298Z
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Chris Callison-Burch
user
CCB
mStyleDistance: Multilingual Style Embeddings and their Evaluation
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 .
Multi-lingual style embeddings trained via contrastive learning outperform existing models across different languages and benchmarks.
style embeddings multilingual style embedding synthetic data contrastive learning STEL-or-Content benchmark authorship verification