Can Sentence Transformers handle languages other than English?
Yes, Sentence Transformers can handle multiple languages beyond English. This capability stems from their underlying architecture, which often uses pretrained multilingual language models like multilingual BERT (mBERT), XLM-RoBERTa (XLM-R), or LaBSE. These models are pretrained on text from dozens or even hundreds of languages, enabling them to generate meaningful embeddings for sentences in those languages. For example, the paraphrase-multilingual-MiniLM-L12-v2 model supports 50+ languages, including German, Chinese, and Spanish. The key is that these models learn shared linguistic patterns during pretraining, allowing them to generalize across languages even if fine-tuning data is limited for some languages.
How are multilingual sentence embeddings achieved? Multilingual embeddings are created by training models on parallel corpora—datasets containing aligned sentences in multiple languages (e.g., translated texts from the European Parliament or United Nations). During training, the model is optimized to map semantically similar sentences from different languages to nearby points in the embedding space. For instance, a sentence in French and its English translation are treated as positive pairs, and their embeddings are pulled closer together via contrastive loss functions like cosine similarity or triplet loss. This alignment ensures that embeddings capture meaning independently of language. Additionally, some models use language-agnostic layers or shared subword tokenizers (e.g., SentencePiece) to further unify representations across languages.
Practical considerations and limitations
While multilingual models work well for many languages, performance can vary depending on the availability of training data. High-resource languages like Spanish or German often have better embeddings than low-resource ones. To address this, techniques like zero-shot transfer are used: models generalize to unseen languages by leveraging similarities in syntax or vocabulary learned during pretraining. For example, a model trained on European languages might still generate useful embeddings for a related but unseen language. Developers can use libraries like sentence-transformers to easily access pretrained multilingual models, but they should validate performance on their specific languages and tasks, as domain-specific nuances may require additional fine-tuning.