Cross-lingual Transfer Performance in Low-Resource African Languages via Language-Specific Data Augmentation

Abstract

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

Research Question

How does the integration of language-specific data augmentation techniques affect the cross-lingual transfer performance of multilingual models on low-resource African languages when evaluated using the XTREME-R benchmark compared to monolingual baselines?

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