Low-resource languages. These are languages which don’t have 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 dominican republic mobile database 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.
NLLB-200: Completely Open-Source
From the outset, NLLB’s different parts have been made open-source. Among the things Meta has made freely available aside from the MT model itself are improvements to its encoder LASER (Language-Agnostic Sentence Representation), the FLORES (Facebook Low-Resource) benchmark used for evaluating the quality of translations, and professionally translated datasets used in training the AI.