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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.07471 |
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| _version_ | 1866908601304481792 |
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| author | Oduwole, Mardiyyah Olajide, Oluwatosin Suleiman, Jamiu Hunja, Faith Awobade, Busayo Adebanjo, Fatimo Akanni, Comfort Igwe, Chinonyelum Ododo, Peace Omoigui, Promise Owodunni, Abraham Kolawole, Steven |
| author_facet | Oduwole, Mardiyyah Olajide, Oluwatosin Suleiman, Jamiu Hunja, Faith Awobade, Busayo Adebanjo, Fatimo Akanni, Comfort Igwe, Chinonyelum Ododo, Peace Omoigui, Promise Owodunni, Abraham Kolawole, Steven |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07471 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation Oduwole, Mardiyyah Olajide, Oluwatosin Suleiman, Jamiu Hunja, Faith Awobade, Busayo Adebanjo, Fatimo Akanni, Comfort Igwe, Chinonyelum Ododo, Peace Omoigui, Promise Owodunni, Abraham Kolawole, Steven Computation and Language 68T50 I.7 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. |
| title | From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation |
| topic | Computation and Language 68T50 I.7 |
| url | https://arxiv.org/abs/2509.07471 |