65 citations · 68 across the 8 of their papers we have counts for
6 papers · 1 filter
TranslateGemma Technical Report
Mara Finkelstein, Isaac Caswell, Tobias Domhan +18
We present TranslateGemma, a suite of open machine translation models based on the Gemma 3 foundation models. To enhance the inherent multilingual capabilities of Gemma 3 for the t…
Mind the Gap... or Not? How Translation Errors and Evaluation Details Skew Multilingual Results
Jan-Thorsten Peter, David Vilar, Tobias Domhan +2
Most current large language models (LLMs) support a wide variety of languages in addition to English, including high-resource languages (e.g. German, Chinese, French), as well as l…
Feeding Two Birds or Favoring One? Adequacy-Fluency Tradeoffs in Evaluation and Meta-Evaluation of Machine Translation
Behzad Shayegh, Jan-Thorsten Peter, David Vilar +4
We investigate the tradeoff between adequacy and fluency in machine translation. We show the severity of this tradeoff at the evaluation level and analyze where popular metrics fal…
Gemma 3 Technical Report
Gemma Team, Aishwarya Kamath, Johan Ferret +209
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…
You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translation
Gergely Flamich, David Vilar, Jan-Thorsten Peter +1
The goal of translation, be it by human or by machine, is, given some text in a source language, to produce text in a target language that simultaneously 1) preserves the meaning o…
There's no Data Like Better Data: Using QE Metrics for MT Data Filtering
Jan-Thorsten Peter, David Vilar, Daniel Deutsch +3
Quality Estimation (QE), the evaluation of machine translation output without the need of explicit references, has seen big improvements in the last years with the use of neural me…