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.CL2024
Generating bilingual example sentences with large language models as lexicography assistants
Raphael Merx, Ekaterina Vylomova, Kemal Kurniawan
We present a study of LLMs' performance in generating and rating example sentences for bilingual dictionaries across languages with varying resource levels: French (high-resource),…