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

arXiv: https://arxiv.org/abs/2405.06818 · HTML: https://mlsanity.com/paper/2405.06818/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 84 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://mlsanity.com/paper/2308.16884/) (2023)
- [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://mlsanity.com/paper/2309.04662/) (2023)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go](https://mlsanity.com/paper/2203.08351/) (2022)
- [AfroBench: How Good are Large Language Models on African Languages?](https://mlsanity.com/paper/2311.07978/) (2023)
- [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://mlsanity.com/paper/2304.06845/) (2023)
- [SERENGETI: Massively Multilingual Language Models for Africa](https://mlsanity.com/paper/2212.10785/) (2022)
- [AfroLID: A Neural Language Identification Tool for African Languages](https://mlsanity.com/paper/2210.11744/) (2022)
- [BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus](https://mlsanity.com/paper/2207.03546/) (2022)
- [AI4D -- African Language Program](https://mlsanity.com/paper/2104.02516/) (2021)
- [Cheetah: Natural Language Generation for 517 African Languages](https://mlsanity.com/paper/2401.01053/) (2024)
- [PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English](https://mlsanity.com/paper/1912.03444/) (2019)
- [English2Gbe: A multilingual machine translation model for {Fon/Ewe}Gbe](https://mlsanity.com/paper/2112.11482/) (2021)

## Built on by (most cited first, 5 of 5)

- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [Translation as a Scalable Proxy for Multilingual Evaluation](https://mlsanity.com/paper/2601.11778/) (2026)
- [MERLIN: Multi-Stage Curriculum Alignment for Multilingual Encoder-LLM Integration in Cross-Lingual Reasoning](https://mlsanity.com/paper/2509.08105/) (2025)
- [The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP](https://mlsanity.com/paper/2510.05644/) (2025)
- [Education-centered critical policy analysis of AI: Ghana's AI strategy as a case](https://mlsanity.com/paper/2608.16910/) (2026)

## Abstract (arXiv)

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.