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

Domain:

natural language processing

Record type:

paper

Creator:

OduOlaSulHun

Host:

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. 8 pages, 3 tables. Exploratory work on Data Augmentation for African Machine Translation

Visit

arxiv.org

Tasks

machine translation

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