CMU & Google Extend Pretrained Models to Thousands of Underrepresented Languages Without Using Monolingual Data

A research team from Carnegie Mellon University and Google systematically explores strategies for leveraging the relatively under-studied resource of bilingual lexicons to adapt pretrained multilingual models to low-resource languages. Their resulting Lexicon-based Adaptation approach produces consistent performance improvements without requiring additional monolingual text.

ai artificial intelligence bert data google machine learning machine learning & data science ml multilingual language model pretrained language model research technology