A Systematic Review of NLP for Ghanaian Languages: Datasets, Models, and a Research Roadmap

arXiv:2405.06818 · cs.CL · Submitted 2024-05-10 · Read on arXiv

cs.CL

Submitted: 2024-05-10

Updated: 2026-09-16

Comments: 8 main pages. Includes an appendix with supplementary methodology and abstract translations in 15 languages

License: http://creativecommons.org/licenses/by/4.0/

The gist: Natural Language Processing (NLP) for Ghana's 73 living indigenous languages remains deeply fragmented, under-resourced, and heavily skewed toward a single language.

Terminology

Abstract

Natural Language Processing (NLP) for Ghana's 73 living indigenous languages remains deeply fragmented, under-resourced, and heavily skewed toward a single language. We present the first systematic review of the Ghanaian NLP landscape, screening 17,000+ publications across four academic databases to critically synthesize 36 core studies spanning datasets, model architectures, and evaluation paradigms. Our analysis exposes a severe resource imbalance: Twi-centric NLP has grown modestly, driven largely by religious-text alignment and crowdsourcing, while the remaining 70+ languages remain almost entirely unaddressed, and dataset releases, model checkpoints, and evaluation practices remain inconsistent and rarely shared across the field. We translate these findings into a prioritized roadmap targeting Ghana's acute regional constraints, dialectal variation, non-standardized orthographies, and the absence of shared infrastructure, offering a replicable template for systematic review and research prioritization in other low-resource language settings.

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