arxiv:2510.05644

The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP

Published on Oct 7

· Submitted by

Sheriff I on Oct 9

University of California, Los Angeles

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Authors:

Sheriff Issaka ,

Persis Boateng ,

Abstract

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.

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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.

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imsheriff

Paper author Paper submitter 3 days ago

This paper introduces the African Languages Lab (ALL Lab), a collaborative research initiative aimed at closing the significant gap in NLP support for African languages, 88% of which are currently underrepresented or ignored in computational linguistics.

In short, the All Lab demonstrates that systematic data collection, open collaboration, and local capacity building can improve NLP performance for low-resourced languages, bridging one of AI’s most urgent linguistic gaps.

quick, bullet-point summary of the key findings from the paper:

🔑 Key Findings from “The African Languages Lab” (arXiv:2510.05644, 2025)

🗂️ Introduces a Large African NLP dataset
- 19 billion text tokens + 12,628 hours of speech across 40 languages.
- Built All Voices — a mobile, community-driven platform enabling direct African↔African translations.
📈 Major performance gains after fine-tuning
- +23.69 ChrF++, +15.34 BLEU, +0.33 COMET (average).
- Models often matched or beat Google Translate for Yoruba, Twi, Arabic; near-parity for Swahili, Hausa, Sesotho.
🌍 Severe underrepresentation revealed
- African languages are 20–70× less studied in NLP research than top global languages.
⚖️ Two digital divides identified
- Text divide: few languages (Amharic, Yoruba, Afrikaans) dominate written data.
- Audio divide: others (Kinyarwanda, Swahili, Arabic) dominate speech data.
💡 Low-resource languages benefit most
- Even minimal data improved translation quality for extremely underserved languages (e.g., Fula, Wolof, Kikongo).
🔬 Different improvement patterns observed
- High responders: Swahili, Hausa, Sesotho.
- Moderate: Igbo, Somali, Shona.
- Challenging: Fon, Wolof, Bambara.
🧪 Metrics show nuanced progress
- Surface metrics (BLEU, ChrF++) and semantic metrics (COMET) don’t always align → need multi-metric evaluation.
🧭 Core takeaway:
- With coordinated data collection, open collaboration, and local participation, African NLP may reach global parity — the gap is technical and social, not inevitable.