187 citations · 268 across the 11 of their papers we have counts for
11 papers
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…
MetricX-25 and GemSpanEval: Google Translate Submissions to the WMT25 Evaluation Shared Task
Juraj Juraska, Tobias Domhan, Mara Finkelstein +5
In this paper, we present our submissions to the unified WMT25 Translation Evaluation Shared Task. For the Quality Score Prediction subtask, we create a new generation of MetricX w…
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…
Same evaluation, more tokens: On the effect of input length for machine translation evaluation using Large Language Models
Tobias Domhan, Dawei Zhu
Accurately evaluating machine-translated text remains a long-standing challenge, particularly for long documents. Recent work has shown that large language models (LLMs) can serve…
Trained MT Metrics Learn to Cope with Machine-translated References
Jannis Vamvas, Tobias Domhan, Sony Trenous +2
Neural metrics trained on human evaluations of MT tend to correlate well with human judgments, but their behavior is not fully understood. In this paper, we perform a controlled ex…
Sockeye 3: Fast Neural Machine Translation with PyTorch
Felix Hieber, Michael Denkowski, Tobias Domhan +11
Sockeye 3 is the latest version of the Sockeye toolkit for Neural Machine Translation (NMT). Now based on PyTorch, Sockeye 3 provides faster model implementations and more advanced…
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation
Tobias Domhan, Eva Hasler, Ke Tran +3
Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words dur…
The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020
Tobias Domhan, Michael Denkowski, David Vilar +3
We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit. New features include a simplified code base through the use of M…
Image Captioning as Neural Machine Translation Task in SOCKEYE
Loris Bazzani, Tobias Domhan, Felix Hieber
Image captioning is an interdisciplinary research problem that stands between computer vision and natural language processing. The task is to generate a textual description of the…
Sockeye: A Toolkit for Neural Machine Translation
Felix Hieber, Tobias Domhan, Michael Denkowski +4
We describe Sockeye (version 1.12), an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready framework for training and apply…