I'm using a multilingual Sentence Transformer, but it doesn't perform well for a particular language — what steps can I take to improve performance for that language?

To improve a multilingual Sentence Transformer’s performance for a specific language, start by addressing data quality and quantity. Multilingual models often underperform for languages with limited training data or structural differences from dominant languages like English. First, verify if your target language has sufficient representation in the model’s training data. If not, augment the dataset with domain-specific or general-purpose text in that language. For example, if working with a low-resource language like Estonian, gather publicly available corpora (e.g., news articles, Wikipedia entries) or use machine translation to generate synthetic parallel sentences paired with high-resource languages. Ensure translated data is accurate, as poor translations can introduce noise. Additionally, filter out low-quality or irrelevant text to avoid skewing embeddings.

Next, fine-tune the model on task-specific data in the target language. Pretrained multilingual models are generalists, so fine-tuning on labeled data aligned with your use case (e.g., semantic similarity, retrieval) can significantly boost performance. For instance, if your task involves clustering customer support tickets in Thai, fine-tune the model on Thai sentences labeled for semantic similarity. If labeled data is scarce, leverage cross-lingual transfer: train on high-resource language data (e.g., English) paired with translations or alignments in the target language. Tools like the Hugging Face datasets library can help manage and balance multilingual datasets. Ensure the fine-tuning process retains multilingual capabilities by including a mix of languages in the training batches.

Finally, optimize preprocessing and tokenization for the target language. Many multilingual models use subword tokenizers (e.g., WordPiece) trained on a mix of languages, which may split words in suboptimal ways for certain scripts or morphologies. For example, agglutinative languages like Finnish or script-heavy languages like Japanese might benefit from custom tokenization rules or switching to a language-specific pretokenizer. Test alternative tokenizers (e.g., MeCab for Japanese) and compare embedding quality on downstream tasks. Additionally, normalize text by handling diacritics, case, or script variations (e.g., converting Traditional to Simplified Chinese) to reduce sparsity. Evaluate changes rigorously using language-specific benchmarks (e.g., XNLI for natural language inference) or custom validation sets to isolate improvements.