OpenWHO: A Document-Level Parallel Corpus for Health Translation in Low-Resource Languages

• URL: http://arxiv.org/abs/2508.16048v5
• Date: Mon, 06 Oct 2025 02:43:38 GMT
• Title: OpenWHO: A Document-Level Parallel Corpus for Health Translation in Low-Resource Languages
• Authors: Raphaël Merx, Hanna Suominen, Trevor Cohn, Ekaterina Vylomova,
• Abstract summary: We introduce OpenWHO, a document-level parallel corpus of 2,978 documents and 26,824 sentences from the World Health Organization's e-learning platform.<n>We evaluate modern large language models (LLMs) against traditional machine translation models.
• Score: 27.956140150633832
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In machine translation (MT), health is a high-stakes domain characterised by widespread deployment and domain-specific vocabulary. However, there is a lack of MT evaluation datasets for low-resource languages in this domain. To address this gap, we introduce OpenWHO, a document-level parallel corpus of 2,978 documents and 26,824 sentences from the World Health Organization's e-learning platform. Sourced from expert-authored, professionally translated materials shielded from web-crawling, OpenWHO spans a diverse range of over 20 languages, of which nine are low-resource. Leveraging this new resource, we evaluate modern large language models (LLMs) against traditional MT models. Our findings reveal that LLMs consistently outperform traditional MT models, with Gemini 2.5 Flash achieving a +4.79 ChrF point improvement over NLLB-54B on our low-resource test set. Further, we investigate how LLM context utilisation affects accuracy, finding that the benefits of document-level translation are most pronounced in specialised domains like health. We release the OpenWHO corpus to encourage further research into low-resource MT in the health domain.

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