1. Improve Data Quality and Relevance Start by ensuring your training or fine-tuning data is high-quality and representative of the target language. Multilingual models often generalize across languages, but performance drops if the target language has limited or noisy data in the original training. For example, if your model underperforms on Vietnamese, gather domain-specific text (e.g., news, social media, or technical documents) in Vietnamese and fine-tune the model on this data. Use parallel datasets (text pairs in the target language and a high-resource language like English) to leverage cross-lingual transfer. Tools like OPUS or Tatoeba provide multilingual corpora for this purpose. Clean the data by removing duplicates, correcting encoding issues, and normalizing text (e.g., handling diacritics or script variations).
2. Adjust Tokenization and Model Configuration Multilingual models often use subword tokenization (e.g., SentencePiece or WordPiece), which may not handle certain languages optimally. For agglutinative languages (e.g., Turkish) or languages with complex morphology, the tokenizer might split words into nonsensical subwords. Try using a language-specific tokenizer or adjusting the tokenizer’s vocabulary size. For example, for Japanese, switch from WordPiece to MeCab-based tokenization. If the model architecture allows, freeze layers responsible for general multilingual features and retrain the top layers with language-specific data. Alternatively, explore monolingual or bilingual models (e.g., LASER or LaBSE) as a starting point for fine-tuning.
3. Leverage Cross-Lingual Transfer and Evaluation If labeled data in the target language is scarce, use machine translation to convert high-resource language data (e.g., English) into the target language for training. For instance, translate English STS (Semantic Textual Similarity) datasets into Swahili and fine-tune the model on the translated pairs. Validate performance with language-specific evaluation benchmarks, such as Flores-101 for low-resource languages, to identify gaps. Additionally, test whether the model’s embeddings align with language-specific features (e.g., using cosine similarity for semantic equivalents). If runtime allows, combine multiple strategies: fine-tune on translated data, adjust tokenization, and use language-specific pretraining (e.g., continuing training on a masked language modeling task for the target language).