title: mStyleDistance: Multilingual Style Embeddings and their Evaluation
summary: 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_authors._id: 67bc90e38915fc3c91098a9e
paper_authors.name: Justin Qiu
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paper_authors.fullname: Justin Q
paper_authors.type: user
paper_authors.user: justinsunqiu
paper_authors._id: 67bc90e38915fc3c91098a9f
paper_authors.name: Jiacheng Zhu
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paper_authors.fullname: Jiacheng Zhu
paper_authors.type: user
paper_authors.user: jjz5463
paper_authors._id: 67bc90e38915fc3c91098aa0
paper_authors.name: Ajay Patel
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paper_authors.fullname: Ajay Patel
paper_authors.type: user
paper_authors.user: AjayP13
paper_authors._id: 67bc90e38915fc3c91098aa1
paper_authors.name: Marianna Apidianaki
paper_authors._id: 67bc90e38915fc3c91098aa2
paper_authors.name: Chris Callison-Burch
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/09f5e87c1f56a1b7f6ef9c5037682285.svg
paper_authors.fullname: Chris Callison-Burch
paper_authors.type: user
paper_authors.user: CCB
paper_title: mStyleDistance: Multilingual Style Embeddings and their Evaluation
paper_summary: 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_ai_summary: Multi-lingual style embeddings trained via contrastive learning outperform existing models across different languages and benchmarks.
paper_ai_keywords: style embeddings multilingual style embedding synthetic data contrastive learning STEL-or-Content benchmark authorship verification