AfriScience-MT: Towards Decolonizing Science in Africa through Text Translation 文章
详细信息
- 来源站点
- ArXiv CS.CL
- 作者
- 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, Vukosi Marivate
- 文章类型
- NEWS
- 语言
- en
- 发布日期
- 2026-05-29
摘要
arXiv:2605.29741v1 Announce Type: new 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\`ub\'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.