mStyleDistance: Multilingual Style Embeddings and their Evaluation
Authors: name: Justin Qiu, name: Jiacheng Zhu, name: Ajay Patel, name: Marianna Apidianaki, name: Chris Callison-Burch

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 .