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More than 7,000 languages are in use throughout the world, but popular translation tools cannot deal with most of them. A translation model that was tested on under-represented languages takes a key step towards a solution.
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- David I. Adelani0
- David I. Adelani
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David I. Adelani is in the Department of Computer Science, University College London Centre for Artificial intelligence, London WC1V 6BH, UK; in the School of Computer Science, McGill University, Montreal, Quebec, Canada, and at Mila — Quebec AI Institute, Montreal, Quebec, Canada.
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Machine-translation models use artificial intelligence (AI) to translate one human language into another — a worthy feat, given the potential for enhanced communication to break down the barriers posed by differences in language and culture. Yet most of these models can interpret only a small fraction of the world’s languages, in part because training them requires online data that don’t exist for many languages. The US technology company Meta has designed a project called No Language Left Behind (NLLB) to change that. Writing in Nature, the NLLB team1 presents a publicly available model that can translate between 204 languages, many of which are used in low- and middle-income countries.
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doi: https://doi.org/10.1038/d41586-024-00964-2
References
NLLB Team. Nature https://doi.org/10.1038/s41586-024-07335-x (2024).
Bahdanau, D., Cho, K. & Bengio, Y. Preprint at arXiv https://doi.org/10.48550/arXiv.1409.0473 (2014).
Sennrich, R., Haddow, B. & Birch, A. Proc. 54th Annu. Meet. Assoc. Comput. Linguist. (Vol. 1: Long Papers), 1715–1725 (2016).
Vaswani, A. et al. Adv. Neural Inf. Process. Syst. 30, 6000–6010 (2017).
Conneau, A. et al. Proc. 58th Annu. Meet. Assoc. Comput. Linguist. 8440–8451 (2020).
Goyal, N. et al. Trans. Assoc. Comput. Linguist. 10, 522–538 (2022).
Adelani, D. I. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2309.07445 (2024).
Robinson, N., Ogayo, P., Mortensen, D. R. & Neubig, G. Proc. Eighth Conf. Mach. Transl. 392–418 (2023).
Bandarkar, L. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2308.16884 (2023).
Adelani, D. et al. Proc. 2022 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. 3053–3070 (2022).
Maillard, J. et al. Proc. 61st Annu. Meet. Assoc. Comput. Linguist. (Vol. 1: Long Papers) 2740–2756 (2023).
Reid, M. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2403.05530 (2024).
Zhang, K. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2402.18025 (2024).
Competing Interests
The author declares no competing interests.
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