@inproceedings{issaka-etal-2026-african,
title = "The {A}frican Languages Lab: A Collaborative Approach to Advancing Low-Resource {A}frican {NLP}",
author = "Issaka, Sheriff and
Wang, Keyi and
Ajibola, Yinka and
Samuel-Ipaye, Oluwatumininu and
Zhang, Zhaoyi and
Jimenez, Nicte Aguillon and
Agyei, Evans Kofi and
Lin, Abraham and
Ramachandran, Rohan and
Mumin, Sadick Abdul and
Nchifor, Faith and
Issah, Mohammed Shuraim and
Gonzalez, Erick Rosas and
Liu, Lieqi and
Kpei, Sylvester and
Osei, Jemimah Kusi and
Ajeneza, Carlene and
Boateng, Persis and
Yeboah, Prisca Adwoa Dufie and
Gabriel, Saadia",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1965/",
pages = "42460--42477",
ISBN = "979-8-89176-390-6",
abstract = "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 empirical analysis. 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 text tokens and 12,628 hours of aligned speech data; (2) extensive experimental validation demonstrating that even modest-scale models, when fine-tuned on targeted language data, achieve substantial improvements over untrained baselines, averaging +23.69 ChrF++, +0.33 COMET, and +15.34 BLEU points across 31 evaluated languages; and (3) a comparative analysis against Google Translate in which a 1B-parameter model matched or surpassed the commercial system in several languages including Yoruba and Twi, revealing that data scarcity, rather than model scale, constitutes the primary bottleneck for low-resource NLP, and suggesting that systematic dataset development yields disproportionate returns for low-resource languages."
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<abstract>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 empirical analysis. 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 text tokens and 12,628 hours of aligned speech data; (2) extensive experimental validation demonstrating that even modest-scale models, when fine-tuned on targeted language data, achieve substantial improvements over untrained baselines, averaging +23.69 ChrF++, +0.33 COMET, and +15.34 BLEU points across 31 evaluated languages; and (3) a comparative analysis against Google Translate in which a 1B-parameter model matched or surpassed the commercial system in several languages including Yoruba and Twi, revealing that data scarcity, rather than model scale, constitutes the primary bottleneck for low-resource NLP, and suggesting that systematic dataset development yields disproportionate returns for low-resource languages.</abstract>
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%0 Conference Proceedings
%T The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP
%A Issaka, Sheriff
%A Wang, Keyi
%A Ajibola, Yinka
%A Samuel-Ipaye, Oluwatumininu
%A Zhang, Zhaoyi
%A Jimenez, Nicte Aguillon
%A Agyei, Evans Kofi
%A Lin, Abraham
%A Ramachandran, Rohan
%A Mumin, Sadick Abdul
%A Nchifor, Faith
%A Issah, Mohammed Shuraim
%A Gonzalez, Erick Rosas
%A Liu, Lieqi
%A Kpei, Sylvester
%A Osei, Jemimah Kusi
%A Ajeneza, Carlene
%A Boateng, Persis
%A Yeboah, Prisca Adwoa Dufie
%A Gabriel, Saadia
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F issaka-etal-2026-african
%X 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 empirical analysis. 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 text tokens and 12,628 hours of aligned speech data; (2) extensive experimental validation demonstrating that even modest-scale models, when fine-tuned on targeted language data, achieve substantial improvements over untrained baselines, averaging +23.69 ChrF++, +0.33 COMET, and +15.34 BLEU points across 31 evaluated languages; and (3) a comparative analysis against Google Translate in which a 1B-parameter model matched or surpassed the commercial system in several languages including Yoruba and Twi, revealing that data scarcity, rather than model scale, constitutes the primary bottleneck for low-resource NLP, and suggesting that systematic dataset development yields disproportionate returns for low-resource languages.
%U https://aclanthology.org/2026.acl-long.1965/
%P 42460-42477
Markdown (Informal)
[The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP](https://aclanthology.org/2026.acl-long.1965/) (Issaka et al., ACL 2026)
ACL
- Sheriff Issaka, Keyi Wang, Yinka Ajibola, Oluwatumininu Samuel-Ipaye, Zhaoyi Zhang, Nicte Aguillon Jimenez, Evans Kofi Agyei, Abraham Lin, Rohan Ramachandran, Sadick Abdul Mumin, Faith Nchifor, Mohammed Shuraim Issah, Erick Rosas Gonzalez, Lieqi Liu, Sylvester Kpei, Jemimah Kusi Osei, Carlene Ajeneza, Persis Boateng, Prisca Adwoa Dufie Yeboah, and Saadia Gabriel. 2026. The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 42460–42477, San Diego, California, United States. Association for Computational Linguistics.