AfriScience-MT: Towards Decolonizing Science in Africa through Text Translation
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| author | Abdulmumin, Idris Gwadabe, Tajuddeen Muhammad, Shamsuddeen Hassan Adelani, David Ifeoluwa Khalo, Nomonde Ahmad, Ibrahim Said Modupe, Abiodun Mumm, Anina Biyela, Sibusiso Rabie, Michelle Havemann, Johanna Rei, Marek Abbott, Jade Marivate, Vukosi |
| author_facet | Abdulmumin, Idris Gwadabe, Tajuddeen Muhammad, Shamsuddeen Hassan Adelani, David Ifeoluwa Khalo, Nomonde Ahmad, Ibrahim Said Modupe, Abiodun Mumm, Anina Biyela, Sibusiso Rabie, Michelle Havemann, Johanna Rei, Marek Abbott, Jade Marivate, Vukosi |
| contents | The dominance of colonial languages in African education and scientific communication limits how hundreds of millions of speakers of African languages access and produce scientific knowledge. A core obstacle is the lack of established scientific terminology in these languages. We introduce AfriScience-MT, a parallel corpus covering six African languages (Amharic, Hausa, Luganda, Northern Sotho, Yorùbá, and isiZulu) across 11 scientific domains. Professional translators, working with expert science communicators, translated plain-language summaries of scientific papers into each target language and created new terms where none existed. We benchmark machine translation systems and large language models in zero-shot, few-shot, and fine-tuned settings. Our results show that closed-source models outperform all open-source models at both the sentence and document levels: GPT-5.4 and Gemini-3.1-Flash-Lite lead with average sentence-level COMET scores of 68.3 and 68.0, respectively, and tie at an average document-level COMET of 48.3. Among open systems, fine-tuned NLLB-1.3B reaches 67.3 at the sentence level, and TranslateGemma-12B reaches 44.0 at the document level with 1-shot in-context learning. We release AfriScience-MT to support benchmarking and document-level scientific MT for African languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29741 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AfriScience-MT: Towards Decolonizing Science in Africa through Text Translation Abdulmumin, Idris Gwadabe, Tajuddeen Muhammad, Shamsuddeen Hassan Adelani, David Ifeoluwa Khalo, Nomonde Ahmad, Ibrahim Said Modupe, Abiodun Mumm, Anina Biyela, Sibusiso Rabie, Michelle Havemann, Johanna Rei, Marek Abbott, Jade Marivate, Vukosi Computation and Language The dominance of colonial languages in African education and scientific communication limits how hundreds of millions of speakers of African languages access and produce scientific knowledge. A core obstacle is the lack of established scientific terminology in these languages. We introduce AfriScience-MT, a parallel corpus covering six African languages (Amharic, Hausa, Luganda, Northern Sotho, Yorùbá, and isiZulu) across 11 scientific domains. Professional translators, working with expert science communicators, translated plain-language summaries of scientific papers into each target language and created new terms where none existed. We benchmark machine translation systems and large language models in zero-shot, few-shot, and fine-tuned settings. Our results show that closed-source models outperform all open-source models at both the sentence and document levels: GPT-5.4 and Gemini-3.1-Flash-Lite lead with average sentence-level COMET scores of 68.3 and 68.0, respectively, and tie at an average document-level COMET of 48.3. Among open systems, fine-tuned NLLB-1.3B reaches 67.3 at the sentence level, and TranslateGemma-12B reaches 44.0 at the document level with 1-shot in-context learning. We release AfriScience-MT to support benchmarking and document-level scientific MT for African languages. |
| title | AfriScience-MT: Towards Decolonizing Science in Africa through Text Translation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.29741 |