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 this https URL .
Abstract (translated)
风格嵌入在风格分析和风格转换中很有用；然而，只有英语的风格嵌入被公开提供。我们引入了多语言StyleDistance（mStyleDistance），这是一个通过合成数据和对比学习训练的多语言风格嵌入模型。我们在九种语言的数据上对模型进行了训练，并建立了一个服务于评估嵌入质量的多语言STEL或内容基准（Wegmann等，2022）。我们还将在涉及不同语言的作者身份验证任务中使用我们的嵌入。结果表明，在这些多语言风格基准测试中，mStyleDistance嵌入超过了现有模型的表现，并且很好地泛化到未见过的特性和语言上。我们在[此处](https://此URL)公开发布了该模型。
URL
https://arxiv.org/abs/2502.15168
