To improve a model's cross-lingual performance through fine-tuning, focus on three key areas: leveraging multilingual pre-trained models, optimizing data strategies, and applying task-specific training techniques. Start with a multilingual base model like mBERT (Multilingual BERT) or XLM-R (Cross-lingual Language Model-RoBERTa), which are trained on text from over 100 languages. These models inherently capture cross-lingual patterns by aligning representations of similar words or phrases across languages during pre-training. Fine-tuning them on a task (e.g., text classification, named entity recognition) while incorporating multilingual data helps adapt these general patterns to your specific use case. For example, if building a sentiment analysis system for Spanish and French, fine-tune XLM-R on labeled data from both languages to align task-specific features.
Data quality and diversity are critical. Use parallel corpora (texts translated into multiple languages) to explicitly teach the model cross-lingual mappings. For instance, the OPUS repository provides parallel datasets like Europarl for European languages. If parallel data is scarce, combine monolingual datasets from multiple languages and use machine translation to augment low-resource languages. However, ensure translated data retains task-specific labels (e.g., translated sentences should preserve sentiment annotations). Balance the dataset across languages to avoid bias—oversample underrepresented languages or apply techniques like dynamic data weighting. For example, if training a question-answering model, include equal proportions of examples from each target language to prevent the model from overfitting to dominant languages like English.
Finally, tailor the training process. Use a two-stage approach: first, fine-tune on high-resource languages with abundant data, then adapt to low-resource languages via continued training. Alternatively, employ parameter-efficient methods like adapter modules—small neural layers inserted into the base model—to specialize for each language without overwriting core multilingual knowledge. For evaluation, test on zero-shot scenarios (e.g., train on English and test on Swahili) to verify true cross-lingual transfer. Tools like Hugging Face’s Transformers library simplify implementation, offering scripts for multilingual fine-tuning. Regularly validate performance per language and iterate—adjust learning rates, add data, or refine preprocessing (e.g., normalizing diacritics in Arabic or Vietnamese). These steps ensure the model generalizes across languages while maintaining task accuracy.