Scaling neural machine translation to 200 languages.

ABSTRACT: The development of neural techniques has opened up new avenues for research in machine translation. Today, neural machine translation (NMT) systems can leverage highly multilingual capacities and even perform zero-shot translation, delivering promising results in terms of language coverage and quality. However, scaling quality NMT requires large volumes of parallel bilingual data, which are not equally available for the 7,000+ languages in the world¹. Focusing on improving the translation qualities of a relatively small group of high-resource languages comes at the expense of directing research attention to low-resource languages, exacerbating digital inequities in the long run. To break this pattern, here we introduce No Language Left Behind-a single massively multilingual mode

SUBMITTER: NLLB Team 

PROVIDER: S-EPMC11208141 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature