@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://preview.aclanthology.org/ingest-acl/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."
}