A Systematic Review on Machine Translation for Low Resource Nigerian Languages

Authors

  • Tijani M. Abdulmusawir Department of Computer Science, School of Technology, Federal Polytechnic Idah, Kogi state, Nigeria
  • A. F. Donfack Kana Department of Computer Science, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Kaduna state, Nigeria
  • Amina H. Abubakar Department of Computer Science, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Kaduna state, Nigeria

DOI:

https://doi.org/10.25299/itjrd.2025.21277

Keywords:

Artificial Inteligence, Domain Adaption, Machine Translation, Low-Resource

Abstract

Nigeria ranks among Africa's most linguistically diverse countries with over 500 indigenous languages, yet machine translation (MT) research remains severely limited for these low-resource languages. This systematic review examines the current state of MT research for Nigerian languages, identifies persistent challenges, and analyzes methodological trends. A systematic literature search was conducted across eleven databases including PubMed, Web of Science, and Scopus from January 2010 to August 2025. Search terms combined machine translation approaches with Nigerian language terms. Studies were screened using PRISMA guidelines requiring original research with evaluation metrics. From 51 papers, 25 duplicates were removed, 7 excluded for selection criteria, and 3 for lack of contribution, resulting in 16 studies. Only 11 Nigerian languages (2.2% of over 500 languages) were covered, creating a 97.8% research gap. Yoruba led with 4 studies, followed by Igala (3), Igbo and Nigerian Pidgin (2 each). Methods evolved from rule-based (4 studies, 2014 to 2021) through SMT (2 studies, 2016 to 2019) to NMT dominance (10 studies, 2018 to 2025). Idiomatic expression handling was the most persistent challenge (16.7%), followed by complex sentences, data scarcity, and domain specificity (each 9.5%). Nigerian MT research shows severe underrepresentation with persistent challenges in idiomatic expressions and data scarcity across all approaches. Neural method adoption reflects global trends but doesn't address resource constraints. Coordinated national approaches prioritizing parallel corpora creation and institutional partnerships are needed to prevent digital divides and support language preservation.

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Published

2025-11-25

How to Cite

M. Abdulmusawir, T., Kana, A. F. D., & Abubakar, A. H. (2025). A Systematic Review on Machine Translation for Low Resource Nigerian Languages. IT Journal Research and Development, 10(2), 46–55. https://doi.org/10.25299/itjrd.2025.21277

Issue

Vol. 10 No. 2 (2025)

Section

Review Article

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Copyright (c) 2025 Tijani M. Abdulmusawir, A. F. Donfack Kana, Amina H. Abubakar

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