53 citations · 59 across the 12 of their papers we have counts for
15 papers
The University of Melbourne WMT 2026 CreoleMT Submission: A Domain-Balanced Approach to Low-Resource Pacific Creole Machine Translation
Raphaël Merx, Nick Thieberger, Ekaterina Vylomova
For our submission to the WMT26 Creole Language Translation Shared Task, we focus on machine translation (MT) models for Pacific creoles: Tok Pisin, Bislama, and Solomon Pijin, wit…
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…
Vavanagi: a Community-run Platform for Documentation of the Hula Language in Papua New Guinea
Bri Olewale, Raphael Merx, Ekaterina Vylomova
We present Vavanagi, a community-run platform for Hula (Vula'a), an Austronesian language of Papua New Guinea with approximately 10,000 speakers. Vavanagi supports crowdsourced Eng…
A Joint Multitask Model for Morpho-Syntactic Parsing
Demian Inostroza, Mel Mistica, Ekaterina Vylomova +2
We present a joint multitask model for the UniDive 2025 Morpho-Syntactic Parsing shared task, where systems predict both morphological and syntactic analyses following novel UD ann…
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…
Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Language
Raphaël Merx, Aso Mahmudi, Katrina Langford +2
This study explores the use of large language models (LLMs) for translating English into Mambai, a low-resource Austronesian language spoken in Timor-Leste, with approximately 200,…
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…
Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
Ekaterina Vylomova, Trevor Cohn, Xuanli He +1
Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation. In contrast to most m…
Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning
Ekaterina Vylomova, Laura Rimell, Trevor Cohn +1
Recent work on word embeddings has shown that simple vector subtraction over pre-trained embeddings is surprisingly effective at capturing different lexical relations, despite lack…
Depth-Gated LSTM
Kaisheng Yao, Trevor Cohn, Katerina Vylomova +2
In this short note, we present an extension of long short-term memory (LSTM) neural networks to using a depth gate to connect memory cells of adjacent layers. Doing so introduces a…