@inproceedings{abdulmumin-etal-2026-afriscience,
title = "{A}fri{S}cience-{MT}: Towards Decolonizing Science in {A}frica through Text Translation",
author = "Abdulmumin, Idris and
Gwadabe, Tajuddeen and
Muhammad, Shamsuddeen Hassan and
Adelani, David Ifeoluwa and
Khalo, Nomonde and
Ahmad, Ibrahim Said and
Modupe, Abiodun and
Mumm, Anina and
Biyela, Sibusiso and
Rabie, Michelle and
Havemann, Johanna and
Rei, Marek and
Abbott, Jade and
Marivate, Vukosi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.757/",
pages = "17506--17555",
ISBN = "979-8-89176-459-0",
abstract = "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, Yor{\`u}b{\'a}, 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 African MT."
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<abstract>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 African MT.</abstract>
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%0 Conference Proceedings
%T AfriScience-MT: Towards Decolonizing Science in Africa through Text Translation
%A Abdulmumin, Idris
%A Gwadabe, Tajuddeen
%A Muhammad, Shamsuddeen Hassan
%A Adelani, David Ifeoluwa
%A Khalo, Nomonde
%A Ahmad, Ibrahim Said
%A Modupe, Abiodun
%A Mumm, Anina
%A Biyela, Sibusiso
%A Rabie, Michelle
%A Havemann, Johanna
%A Rei, Marek
%A Abbott, Jade
%A Marivate, Vukosi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F abdulmumin-etal-2026-afriscience
%X 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 African MT.
%U https://aclanthology.org/2026.emnlp-main.757/
%P 17506-17555
Markdown (Informal)
[AfriScience-MT: Towards Decolonizing Science in Africa through Text Translation](https://aclanthology.org/2026.emnlp-main.757/) (Abdulmumin et al., EMNLP 2026)
ACL
- Idris Abdulmumin, Tajuddeen Gwadabe, Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Nomonde Khalo, Ibrahim Said Ahmad, Abiodun Modupe, Anina Mumm, Sibusiso Biyela, Michelle Rabie, Johanna Havemann, Marek Rei, Jade Abbott, and Vukosi Marivate. 2026. AfriScience-MT: Towards Decolonizing Science in Africa through Text Translation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 17506–17555, Budapest, Hungary. Association for Computational Linguistics.