This research introduces innovative cross-lingual transfer techniques to extend NLP capabilities beyond high-resource languages like English to the majority of world languages with limited data.

• Developed Medical mT5, the first multilingual text-to-text medical model
• Enables effective knowledge transfer between high-resource and low-resource languages
• Addresses critical data scarcity challenges faced by most global languages
• Creates new opportunities for multilingual medical applications previously unavailable

This breakthrough has significant implications for medical settings, enabling healthcare applications in previously underserved languages and potentially improving access to medical AI tools globally.

Cross-Lingual Transfer for Low-Resource Natural Language Processing