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

arXiv 2509.07471 ↗First posted 9 Sep 2025cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 28 references that are in the map. First posted on arXiv 9 Sep 2025.

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References · 23

  1. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
  2. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,380
  3. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  4. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
  5. 2023A Comprehensive Overview of Large Language Modelscited by 648
  6. 2024Large Language Models: A Surveycited by 525
  7. 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 370
  8. 2017Six Challenges for Neural Machine Translationcited by 335
  9. 2017Data Augmentation for Low-Resource Neural Machine Translationcited by 139
  10. 2018Achieving Human Parity on Automatic Chinese to English News Translationcited by 119
  11. 2022A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translationcited by 98
  12. 2021Survey of Low-Resource Machine Translationcited by 92
11 more
  1. 2018Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluationcited by 88
  2. 2016Reward Augmented Maximum Likelihood for Neural Structured Predictioncited by 81
  3. 2016Edinburgh Neural Machine Translation Systems for WMT 16cited by 76
  4. 2018SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translationcited by 72
  5. 2018Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translationcited by 58
  6. 2018Regularizing Neural Machine Translation by Target-bidirectional Agreementcited by 40
  7. 2019Low-Resource Corpus Filtering using Multilingual Sentence Embeddingscited by 25
  8. 2018Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translationcited by 19
  9. 2023Binarized Neural Machine Translationcited by 13
  10. 2023Data Augmentation for Neural Machine Translation using Generative Language Modelcited by 12
  11. 2021Sentence Concatenation Approach to Data Augmentation for Neural Machine Translationcited by 8

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