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Meta’s AI can translate dozens of under-resourced languages

Simon Osuji by Simon Osuji
June 7, 2024
in Artificial Intelligence
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Meta’s AI can translate dozens of under-resourced languages
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Meta's AI can translate dozens of under-resourced languages
Architecture of the LASER3 teacher-student approach. Credit: Nature (2024). DOI: 10.1038/s41586-024-07335-x

The technology behind Meta’s artificial intelligence model, which can translate 200 different languages, is described in a paper published in Nature. The model expands the number of languages that can be translated via machine translation.

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Neural machine translation models utilize artificial neural networks to translate languages. These models typically need a large amount of accessible data online to train with, which may not be publicly, cheaply, or commonly available for some languages, termed “low-resource languages.” Increasing a model’s linguistic output in terms of the number of languages it translates could negatively affect the quality of the model’s translations.

Marta Costa-jussà and the No Language Left Behind (NLLB) team have developed a cross-language approach, which allows neural machine translation models to learn how to translate low-resource languages using their pre-existing ability to translate high-resource languages.

As a result, the researchers have developed an online multilingual translation tool, called NLLB-200, that includes 200 languages, contains three times as many low-resource languages as high-resource languages, and performs 44% better than pre-existing systems.

Given that the researchers only had access to 1,000–2,000 samples of many low-resource languages, to increase the volume of training data for NLLB-200 they utilized a language identification system to identify more instances of those given dialects. The team also mined bilingual textual data from Internet archives, which helped improve the quality of translations NLLB-200 provided.

The authors note that this tool could help people speaking rarely translated languages to access the Internet and other technologies. Additionally, they highlight education as a particularly significant application, as the model could help those speaking low-resource languages access more books and research articles. However, Costa-jussà and co-authors acknowledge that mistranslations may still occur.

More information:
Scaling neural machine translation to 200 languages, Nature (2024). DOI: 10.1038/s41586-024-07335-x

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Nature Publishing Group

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Meta’s AI can translate dozens of under-resourced languages (2024, June 7)
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