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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.05644 |
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| _version_ | 1866908579224616960 |
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| author | Issaka, Sheriff Wang, Keyi Ajibola, Yinka Samuel-Ipaye, Oluwatumininu Zhang, Zhaoyi Jimenez, Nicte Aguillon Agyei, Evans Kofi Lin, Abraham Ramachandran, Rohan Mumin, Sadick Abdul Nchifor, Faith Shuraim, Mohammed Liu, Lieqi Gonzalez, Erick Rosas Kpei, Sylvester Osei, Jemimah Ajeneza, Carlene Boateng, Persis Yeboah, Prisca Adwoa Dufie Gabriel, Saadia |
| author_facet | Issaka, Sheriff Wang, Keyi Ajibola, Yinka Samuel-Ipaye, Oluwatumininu Zhang, Zhaoyi Jimenez, Nicte Aguillon Agyei, Evans Kofi Lin, Abraham Ramachandran, Rohan Mumin, Sadick Abdul Nchifor, Faith Shuraim, Mohammed Liu, Lieqi Gonzalez, Erick Rosas Kpei, Sylvester Osei, Jemimah Ajeneza, Carlene Boateng, Persis Yeboah, Prisca Adwoa Dufie Gabriel, Saadia |
| contents | Despite representing nearly one-third of the world's languages, African languages remain critically underserved by modern NLP technologies, with 88\% classified as severely underrepresented or completely ignored in computational linguistics. We present the African Languages Lab (All Lab), a comprehensive research initiative that addresses this technological gap through systematic data collection, model development, and capacity building. Our contributions include: (1) a quality-controlled data collection pipeline, yielding the largest validated African multi-modal speech and text dataset spanning 40 languages with 19 billion tokens of monolingual text and 12,628 hours of aligned speech data; (2) extensive experimental validation demonstrating that our dataset, combined with fine-tuning, achieves substantial improvements over baseline models, averaging +23.69 ChrF++, +0.33 COMET, and +15.34 BLEU points across 31 evaluated languages; and (3) a structured research program that has successfully mentored fifteen early-career researchers, establishing sustainable local capacity. Our comparative evaluation against Google Translate reveals competitive performance in several languages while identifying areas that require continued development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05644 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP Issaka, Sheriff Wang, Keyi Ajibola, Yinka Samuel-Ipaye, Oluwatumininu Zhang, Zhaoyi Jimenez, Nicte Aguillon Agyei, Evans Kofi Lin, Abraham Ramachandran, Rohan Mumin, Sadick Abdul Nchifor, Faith Shuraim, Mohammed Liu, Lieqi Gonzalez, Erick Rosas Kpei, Sylvester Osei, Jemimah Ajeneza, Carlene Boateng, Persis Yeboah, Prisca Adwoa Dufie Gabriel, Saadia Computation and Language Artificial Intelligence Despite representing nearly one-third of the world's languages, African languages remain critically underserved by modern NLP technologies, with 88\% classified as severely underrepresented or completely ignored in computational linguistics. We present the African Languages Lab (All Lab), a comprehensive research initiative that addresses this technological gap through systematic data collection, model development, and capacity building. Our contributions include: (1) a quality-controlled data collection pipeline, yielding the largest validated African multi-modal speech and text dataset spanning 40 languages with 19 billion tokens of monolingual text and 12,628 hours of aligned speech data; (2) extensive experimental validation demonstrating that our dataset, combined with fine-tuning, achieves substantial improvements over baseline models, averaging +23.69 ChrF++, +0.33 COMET, and +15.34 BLEU points across 31 evaluated languages; and (3) a structured research program that has successfully mentored fifteen early-career researchers, establishing sustainable local capacity. Our comparative evaluation against Google Translate reveals competitive performance in several languages while identifying areas that require continued development. |
| title | The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.05644 |