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Meta’s NLLB-200 AI model improves translation quality by 44% Posted on : Jul 09 - 2022

Meta has unveiled a new AI model called NLLB-200 that can translate 200 languages and improves quality by an average of 44 percent.

Translation apps have been fairly adept at the most popular languages for some time. Even when they don’t offer a perfect translation, it’s normally close enough for the native speaker to understand.

However, there are hundreds of millions of people in regions with many languages – like Africa and Asia – that still suffer from poor translation services.

The metaverse aims to be borderless. To enable that, translation services will have to quickly offer accurate translations.

“As the metaverse begins to take shape, the ability to build technologies that work well in a wider range of languages will help to democratise access to immersive experiences in virtual worlds,” the company explained.

According to Meta, NLLB-200 scored 44 percent higher in the “quality” of translations compared to previous AI research. For some African and Indian-based languages, NLLB-200’s translations were more than 70 percent more accurate.

Meta created a dataset called FLORES-200 to evaluate and improve NLLB-200. The dataset enables researchers to assess FLORES-200’s performance “in 40,000 different language directions.”

Both NLLB-200 and FLORES-200 are being opened to developers to help build on Meta’s work and improve their own translation tools.

Meta has a pool of up to $200,000 in grants for researchers and nonprofit organisations that wish to use NLLB-200 for impactful uses focused on sustainability, food security, gender-based violence, education, or other areas that support UN Sustainable Development Goals.

However, not everyone is fully convinced by Meta’s latest breakthrough.

“It’s worth bearing in mind, despite the hype, that these models are not the cure-all that they may first appear. The models that Meta uses are massive, unwieldy beasts. So, when you get into the minutiae of individualised use-cases, they can easily find themselves out of their depth – overgeneralised and incapable of performing the specific tasks required of them,” commented Victor Botev, CTO at Iris.ai. View more