82 citations · 85 across the 13 of their papers we have counts for
18 papers · 1 filter
Contrastive ESA: Human Evaluation of Multiple Translations at Once
Vilém Zouhar, Roman Grundkiewicz, Sara Rajaee +6
Current human evaluation of machine translation typically assesses single outputs in isolation, a paradigm that suffers from high annotator noise and cost. We introduce Contrastive…
AI translation of literary texts is "fine", but readers still prefer human translations
Yves Ferstler, Adam Podoxin, Ty Brassington +5
AI translation of literary works is increasingly common. While the content may be rendered adequately, we do not know enough about how readers experience it in terms of immersivene…
PEAR: Pairwise Evaluation for Automatic Relative Scoring in Machine Translation
Lorenzo Proietti, Roman Grundkiewicz, Matt Post
We present PEAR (Pairwise Evaluation for Automatic Relative Scoring), a supervised quality estimation (QE) metric family that reframes reference-free machine translation (MT) evalu…
Preliminary Ranking of WMT25 General Machine Translation Systems
Tom Kocmi, Eleftherios Avramidis, Rachel Bawden +25
We present the preliminary rankings of machine translation (MT) systems submitted to the WMT25 General Machine Translation Shared Task, as determined by automatic evaluation metric…
On Instruction-Finetuning Neural Machine Translation Models
Vikas Raunak, Roman Grundkiewicz, Marcin Junczys-Dowmunt
In this work, we introduce instruction finetuning for Neural Machine Translation (NMT) models, which distills instruction following capabilities from Large Language Models (LLMs) i…
PyMarian: Fast Neural Machine Translation and Evaluation in Python
Thamme Gowda, Roman Grundkiewicz, Elijah Rippeth +2
The deep learning language of choice these days is Python; measured by factors such as available libraries and technical support, it is hard to beat. At the same time, software wri…