3 papers
cs.CL2025
OpenWHO: A Document-Level Parallel Corpus for Health Translation in Low-Resource Languages
Raphaël Merx, Hanna Suominen, Trevor Cohn +1
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 dataset…
cs.CL2025
TULUN: Transparent and Adaptable Low-resource Machine Translation
Raphaël Merx, Hanna Suominen, Lois Hong +3
Machine translation (MT) systems that support low-resource languages often struggle on specialized domains. While researchers have proposed various techniques for domain adaptation…
cs.CL2025
Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation service
Raphael Merx, Adérito José Guterres Correia, Hanna Suominen +1
Low-resource machine translation (MT) presents a diversity of community needs and application challenges that remain poorly understood. To complement surveys and focus groups, whic…