6 papers · 1 filter
Context Volume Drives Performance: Tackling Domain Shift in Extremely Low-Resource Translation via RAG
David Samuel Setiawan, Raphaël Merx, Jey Han Lau
Neural Machine Translation (NMT) models for low-resource languages suffer significant performance degradation under domain shift. We quantify this challenge using Dhao, an indigeno…
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…
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…
LSC-Eval: A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Data
Naomi Baes, Raphaël Merx, Nick Haslam +2
Lexical Semantic Change (LSC) provides insight into cultural and social dynamics. Yet, the validity of methods for measuring different kinds of LSC remains unestablished due to the…
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),…
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…