mStyleDistance: Multilingual Style Embeddings and their Evaluation
This work addresses the problem of stylistic analysis and style transfer for researchers and practitioners in multilingual NLP, though it is incremental as it extends existing English methods to multiple languages.
The paper tackled the lack of multilingual style embeddings by introducing mStyleDistance, a model trained on nine languages using synthetic data and contrastive learning, which outperformed existing models on multilingual benchmarks and showed good generalization to unseen features and languages.
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 .