mStyleDistance: Multilingual Style Embeddings and their Evaluation

• URL: http://arxiv.org/abs/2502.15168v1
• Date: Fri, 21 Feb 2025 03:11:41 GMT
• Title: mStyleDistance: Multilingual Style Embeddings and their Evaluation
• Authors: Justin Qiu, Jiacheng Zhu, Ajay Patel, Marianna Apidianaki, Chris Callison-Burch,
• Abstract summary: We introduce Multilingual StyleDistance, a style embedding model trained using synthetic data and contrastive learning.<n>We train the model on data from nine languages and create a multilingual STEL-or-Content benchmark.<n>Our results show that mStyleDistance embeddings outperform existing models on these multilingual style benchmarks and generalize well to unseen features and languages.
• Score: 45.24752717803745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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