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

• URL: http://arxiv.org/abs/2510.05644v1
• Date: Tue, 07 Oct 2025 07:42:52 GMT
• Title: The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP
• Authors: Sheriff Issaka, Keyi Wang, Yinka Ajibola, Oluwatumininu Samuel-Ipaye, Zhaoyi Zhang, Nicte Aguillon Jimenez, Evans Kofi Agyei, Abraham Lin, Rohan Ramachandran, Sadick Abdul Mumin, Faith Nchifor, Mohammed Shuraim, Lieqi Liu, Erick Rosas Gonzalez, Sylvester Kpei, Jemimah Osei, Carlene Ajeneza, Persis Boateng, Prisca Adwoa Dufie Yeboah, Saadia Gabriel,
• Abstract summary: Despite representing nearly one-third of the world's languages, African languages remain critically underserved by modern NLP technologies.<n>We present the African Languages Lab, a comprehensive research initiative that addresses this technological gap through systematic data collection, model development, and capacity building.
• Score: 4.188487384419692
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model [66.17354128553244]
  Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data.<n>We investigate how different training mixes tip the scale for different groups of languages.<n>We train Centurio, a 100-language LVLM, offering state-of-the-art performance in an evaluation covering 14 tasks and 56 languages.
  arXiv  Detail & Related papers  (2025-01-09T10:26:14Z)
• Bridging the Data Provenance Gap Across Text, Speech and Video [67.72097952282262]
  We conduct the largest and first-of-its-kind longitudinal audit across modalities of popular text, speech, and video datasets.<n>Our manual analysis covers nearly 4000 public datasets between 1990-2024, spanning 608 languages, 798 sources, 659 organizations, and 67 countries.<n>We find that multimodal machine learning applications have overwhelmingly turned to web-crawled, synthetic, and social media platforms, such as YouTube, for their training sets.
  arXiv  Detail & Related papers  (2024-12-19T01:30:19Z)
• Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages [55.36534539177367]
  This paper introduces Pangea, a multilingual multimodal large language model (MLLM) trained on a diverse 6M instruction dataset spanning 39 languages.<n>P Pangea significantly outperforms existing open-source models in multilingual settings and diverse cultural contexts.<n>We fully open-source our data, code, and trained checkpoints, to facilitate the development of inclusive and robust multilingual MLLMs.
  arXiv  Detail & Related papers  (2024-10-21T16:19:41Z)
• Cultural Fidelity in Large-Language Models: An Evaluation of Online Language Resources as a Driver of Model Performance in Value Representation [0.0]
  We show that the ability of GPT-4o to reflect societal values of a country correlates with the availability of digital resources in that language. Weaker performance in low-resource languages, especially prominent in the Global South, may worsen digital divides.
  arXiv  Detail & Related papers  (2024-10-14T13:33:00Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• DN at SemEval-2023 Task 12: Low-Resource Language Text Classification via Multilingual Pretrained Language Model Fine-tuning [0.0]
  Most existing models and datasets for sentiment analysis are developed for high-resource languages, such as English and Chinese. The AfriSenti-SemEval 2023 Shared Task 12 aims to fill this gap by evaluating sentiment analysis models on low-resource African languages. We present our solution to the shared task, where we employed different multilingual XLM-R models with classification head trained on various data.
  arXiv  Detail & Related papers  (2023-05-04T07:28:45Z)
• MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition [55.95128479289923]
  African languages are spoken by over a billion people, but are underrepresented in NLP research and development. We create the largest human-annotated NER dataset for 20 African languages. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points.
  arXiv  Detail & Related papers  (2022-10-22T08:53:14Z)
• AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages [94.75849612191546]
  AfroMT is a standardized, clean, and reproducible machine translation benchmark for eight widely spoken African languages. We develop a suite of analysis tools for system diagnosis taking into account the unique properties of these languages. We demonstrate significant improvements when pretraining on 11 languages, with gains of up to 2 BLEU points over strong baselines.
  arXiv  Detail & Related papers  (2021-09-10T07:45:21Z)
• AI4D -- African Language Program [0.21960481478626018]
  This work details the AI4D - African Language Program, a 3-part project that incentivised the crowd-sourcing, collection and curation of language datasets. Key outcomes of the work so far include 1) the creation of 9+ open source, African language datasets annotated for a variety of ML tasks, and 2) the creation of baseline models for these datasets.
  arXiv  Detail & Related papers  (2021-04-06T13:51:16Z)
• Lanfrica: A Participatory Approach to Documenting Machine Translation Research on African Languages [0.012691047660244334]
  Africa has the highest language diversity, with 1500-2000 documented languages and many more undocumented or extinct languages. This makes it hard to keep track of the MT research, models and dataset that have been developed for some of them. Online platforms can be useful creating accessibility to researches, benchmarks and datasets in these African languages.
  arXiv  Detail & Related papers  (2020-08-03T18:14:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.