No Language Left Behind: A Boon For Underrepresented Languages
Posted: Sat Feb 08, 2025 8:25 am
First of its kind. Multilingual machine translation models exist, but none on the scale of what Meta has done. NLLB-200 far surpasses Meta’s own previous M2M-100 model, which could translate among 100 languages without using English as an intermediary.
Open-sourced models. This means that the code for NLLB-200 is freely available for anyone, particularly researchers, to examine and develop.
Evaluated, high-quality translations. Benchmarks for assessing the quality of multilingual MT models are necessary for comparing different kinds. Meta has created one capable of accommodating NLLB-200’s massive linguistic scope.
Low-resource languages. These are languages which don’t have bolivia mobile database much language data available on the web, and thus don’t receive as much attention in the development of MT. NLLB-200 may be the single most concerted effort to include them to date.
The most obvious gain from this development is the attention it brings to languages underrepresented on the internet, or what the MT community refers to as “low-resource languages”.
MT research and development has tended to focus on a small subset of languages for which data is readily available, and for which there is more economic incentive.
This means that as the technology for MT develops, the gains from it will be distributed unevenly among languages, with the high-resource languages gaining more of an advantage and higher-quality translations.
With No Language Left Behind, Meta is making a massive effort to include more languages into the mix than ever before.
Open-sourced models. This means that the code for NLLB-200 is freely available for anyone, particularly researchers, to examine and develop.
Evaluated, high-quality translations. Benchmarks for assessing the quality of multilingual MT models are necessary for comparing different kinds. Meta has created one capable of accommodating NLLB-200’s massive linguistic scope.
Low-resource languages. These are languages which don’t have bolivia mobile database much language data available on the web, and thus don’t receive as much attention in the development of MT. NLLB-200 may be the single most concerted effort to include them to date.
The most obvious gain from this development is the attention it brings to languages underrepresented on the internet, or what the MT community refers to as “low-resource languages”.
MT research and development has tended to focus on a small subset of languages for which data is readily available, and for which there is more economic incentive.
This means that as the technology for MT develops, the gains from it will be distributed unevenly among languages, with the high-resource languages gaining more of an advantage and higher-quality translations.
With No Language Left Behind, Meta is making a massive effort to include more languages into the mix than ever before.