The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP
This addresses the critical gap in NLP technologies for low-resource African languages, which affects billions of speakers, though it is incremental as it builds on existing methods like fine-tuning.
The paper tackles the underrepresentation of African languages in NLP by introducing the African Languages Lab, which created a large multi-modal dataset for 40 languages and achieved average improvements of +23.69 ChrF++, +0.33 COMET, and +15.34 BLEU points over baselines through fine-tuning.
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.