The African Languages Lab: A Collaborative Approach to Advancing
  Low-Resource African NLP
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.
68e70c147ae125f9582e68a5
Sheriff Issaka
claimed_verified
2025-10-09T06:50:40.784Z
66feebe57d722f0879d6b247
/avatars/f9194aa76da3ea2b0212e9ef6b5093d1.svg
Sheriff I
user
imsheriff
68e70c147ae125f9582e68a6
Keyi Wang
68e70c147ae125f9582e68a7
Yinka Ajibola
68e70c147ae125f9582e68a8
Oluwatumininu Samuel-Ipaye
68e70c147ae125f9582e68a9
Zhaoyi Zhang
68e70c147ae125f9582e68aa
Nicte Aguillon Jimenez
68e70c147ae125f9582e68ab
Evans Kofi Agyei
68e70c147ae125f9582e68ac
Abraham Lin
68e70c147ae125f9582e68ad
Rohan Ramachandran
68e70c147ae125f9582e68ae
Sadick Abdul Mumin
68e70c147ae125f9582e68af
Faith Nchifor
68e70c147ae125f9582e68b0
Mohammed Shuraim
68e70c147ae125f9582e68b1
Lieqi Liu
68e70c147ae125f9582e68b2
Erick Rosas Gonzalez
68e70c147ae125f9582e68b3
Sylvester Kpei
68e70c147ae125f9582e68b4
Jemimah Osei
68e70c147ae125f9582e68b5
Carlene Ajeneza
68e70c147ae125f9582e68b6
Persis Boateng
claimed_verified
2025-10-10T09:37:44.403Z
68e7c78ab3ddb12973d2f5a1
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/O8F6AmIh912SwBo9sTAKW.png
Persis Boateng
user
pboateng
68e70c147ae125f9582e68b7
Prisca Adwoa Dufie Yeboah
68e70c147ae125f9582e68b8
Saadia Gabriel
The African Languages Lab: A Collaborative Approach to Advancing
  Low-Resource African NLP
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.
The African Languages Lab addresses the underserved status of African languages in NLP by creating a large dataset and demonstrating improved model performance through fine-tuning.
multi-modal speech and text dataset fine-tuning ChrF++ COMET BLEU Google Translate