From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation

Mardiyyah Oduwole, Oluwatosin Olajide, Jamiu Suleiman, Faith Hunja, Busayo Awobade +7 more
9/9/2025
cs.CL

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Abstract

The linguistic diversity across the African continent presents different challenges and opportunities for machine translation. This study explores the effects of data augmentation techniques in improving translation systems in low-resource African languages. We focus on two data augmentation techniques: sentence concatenation with back translation and switch-out, applying them across six African languages. Our experiments show significant improvements in machine translation performance, with a minimum increase of 25\% in BLEU score across all six languages. We provide a comprehensive analysis and highlight the potential of these techniques to improve machine translation systems for low-resource languages, contributing to the development of more robust translation systems for under-resourced languages.

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Cite this paper

@article{oduwole2025from,
  title  = {From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation},
  author = {Mardiyyah Oduwole and Oluwatosin Olajide and Jamiu Suleiman and Faith Hunja and Busayo Awobade and Fatimo Adebanjo and Comfort Akanni and Chinonyelum Igwe and Peace Ododo and Promise Omoigui and Abraham Owodunni and Steven Kolawole},
  year   = {2025},
  eprint = {2509.07471},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.07471v2}
}

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