The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP
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 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.
Cite
@article{arxiv.2510.05644,
title = {The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP},
author = {Sheriff Issaka and Keyi Wang and Yinka Ajibola and Oluwatumininu Samuel-Ipaye and Zhaoyi Zhang and Nicte Aguillon Jimenez and Evans Kofi Agyei and Abraham Lin and Rohan Ramachandran and Sadick Abdul Mumin and Faith Nchifor and Mohammed Shuraim and Lieqi Liu and Erick Rosas Gonzalez and Sylvester Kpei and Jemimah Osei and Carlene Ajeneza and Persis Boateng and Prisca Adwoa Dufie Yeboah and Saadia Gabriel},
journal= {arXiv preprint arXiv:2510.05644},
year = {2025}
}