1 citations · 2 across the 8 of their papers we have counts for
8 papers
Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent
Raphael Merx, Ekaterina Vylomova, Trevor Cohn
Translation quality depends on purpose: the same source text demands different translations depending on audience, tone, and communicative intent. Yet MT models and metrics treat t…
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…
Language-Specific Latent Process Hinders Cross-Lingual Performance
Zheng Wei Lim, Alham Fikri Aji, Trevor Cohn
Large language models (LLMs) are demonstrably capable of cross-lingual transfer, but can produce inconsistent output when prompted with the same queries written in different langua…
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…
Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM
Zheng Wei Lim, Nitish Gupta, Honglin Yu +1
Multilingual large language models (LLMs) are great translators, but this is largely limited to high-resource languages. For many LLMs, translating in and out of low-resource langu…
Simpson's Paradox and the Accuracy-Fluency Tradeoff in Translation
Zheng Wei Lim, Ekaterina Vylomova, Trevor Cohn +1
A good translation should be faithful to the source and should respect the norms of the target language. We address a theoretical puzzle about the relationship between these object…
Predicting Human Translation Difficulty with Neural Machine Translation
Zheng Wei Lim, Ekaterina Vylomova, Charles Kemp +1
Human translators linger on some words and phrases more than others, and predicting this variation is a step towards explaining the underlying cognitive processes. Using data from…