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Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead

Jesujoba O. Alabi, Michael A. Hedderich, David Ifeoluwa Adelani, Dietrich Klakow

TL;DR

The paper investigates the state of NLP for African languages over the past five years, highlighting how Africa’s linguistic diversity remains underrepresented in cutting-edge NLP and LLMs. It conducts a systematic, multi-source literature survey to analyze 884 papers across languages, tasks, datasets, and modeling approaches, revealing growth driven by community initiatives and targeted funding, yet persistent imbalances in language coverage and resource availability. Key findings show rapid publication growth, heavy emphasis on NLU and MT, widespread use of translation-based datasets, and reliance on transfer learning and other low-resource techniques to bridge gaps. The work offers concrete directions—expanding beyond top languages, creating culturally grounded datasets, advancing multimodal and speech capabilities, developing African-centric LLMs, and strengthening community-led research and funding—to steer inclusive, sustainable NLP development for African languages.

Abstract

With over 2,000 languages and potentially millions of speakers, Africa represents one of the richest linguistic regions in the world. Yet, this diversity is scarcely reflected in state-of-the-art natural language processing (NLP) systems and large language models (LLMs), which predominantly support a narrow set of high-resource languages. This exclusion not only limits the reach and utility of modern NLP technologies but also risks widening the digital divide across linguistic communities. Nevertheless, NLP research on African languages is active and growing. In recent years, there has been a surge of interest in this area, driven by several factors-including the creation of multilingual language resources, the rise of community-led initiatives, and increased support through funding programs. In this survey, we analyze 884 research papers on NLP for African languages published over the past five years, offering a comprehensive overview of recent progress across core tasks. We identify key trends shaping the field and conclude by outlining promising directions to foster more inclusive and sustainable NLP research for African languages.

Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead

TL;DR

The paper investigates the state of NLP for African languages over the past five years, highlighting how Africa’s linguistic diversity remains underrepresented in cutting-edge NLP and LLMs. It conducts a systematic, multi-source literature survey to analyze 884 papers across languages, tasks, datasets, and modeling approaches, revealing growth driven by community initiatives and targeted funding, yet persistent imbalances in language coverage and resource availability. Key findings show rapid publication growth, heavy emphasis on NLU and MT, widespread use of translation-based datasets, and reliance on transfer learning and other low-resource techniques to bridge gaps. The work offers concrete directions—expanding beyond top languages, creating culturally grounded datasets, advancing multimodal and speech capabilities, developing African-centric LLMs, and strengthening community-led research and funding—to steer inclusive, sustainable NLP development for African languages.

Abstract

With over 2,000 languages and potentially millions of speakers, Africa represents one of the richest linguistic regions in the world. Yet, this diversity is scarcely reflected in state-of-the-art natural language processing (NLP) systems and large language models (LLMs), which predominantly support a narrow set of high-resource languages. This exclusion not only limits the reach and utility of modern NLP technologies but also risks widening the digital divide across linguistic communities. Nevertheless, NLP research on African languages is active and growing. In recent years, there has been a surge of interest in this area, driven by several factors-including the creation of multilingual language resources, the rise of community-led initiatives, and increased support through funding programs. In this survey, we analyze 884 research papers on NLP for African languages published over the past five years, offering a comprehensive overview of recent progress across core tasks. We identify key trends shaping the field and conclude by outlining promising directions to foster more inclusive and sustainable NLP research for African languages.

Paper Structure

This paper contains 23 sections, 7 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Surveys
  3. Survey Methodology
  4. Literature Collection
  5. Filtering
  6. Manual Annotation
  7. Analysis Result
  8. How much do researchers publish?
  9. What languages do researchers work on?
  10. What tasks do researchers work on?
  11. What resources are available?
  12. What are the efficient or low-resource techniques used?
  13. What are the other themes?
  14. Discussion and Future Directions
  15. Conclusion
  16. ...and 8 more sections

Figures (7)

  • Figure 1: NLP research distribution across Africa by language coverage. Darker green indicate more papers on languages spoken in each country.
  • Figure 2: Inclusion flow of the systematic review.
  • Figure 3: Distribution of papers by publication year.
  • Figure 4: Top 20 languages by paper count;$\dagger$ indicates languages among the top 20 spoken in Africa.
  • Figure 5: Top 20 language families by number of unique African languages studied.
  • ...and 2 more figures