3 citations · 6 across the 19 of their papers we have counts for
9 papers · 1 filter
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…
MQM Re-Annotation: A Technique for Collaborative Evaluation of Machine Translation
Parker Riley, Daniel Deutsch, Mara Finkelstein +3
Human evaluation of machine translation is in an arms race with translation model quality: as our models get better, our evaluation methods need to be improved to ensure that quali…
Generating Difficult-to-Translate Texts
Vilém Zouhar, Wenda Xu, Parker Riley +4
Machine translation benchmarks sourced from the real world are quickly obsoleted, due to most examples being easy for state-of-the-art translation models. This limits the benchmark…
Don't Sweat the Small Stuff: Segment-Level Meta-Evaluation Based on Pairwise Difference Correlation
Colten DiIanni, Daniel Deutsch
This paper introduces Pairwise Difference Pearson (PDP), a novel segment-level meta-evaluation metric for Machine Translation (MT) that address limitations in previous Pearson's $ρ…
Searching the Internet for Challenging Benchmarks at Scale
Wenda Xu, Vilém Zouhar, Parker Riley +3
Many static benchmarks are beginning to saturate: as models rapidly improve, they achieve near-perfect scores on fixed test sets, leaving little headroom to expose genuine model we…
When LLMs Benchmark Themselves: Deconstructing Self-Bias in Automated Evaluation
Wenda Xu, Sweta Agrawal, Vilém Zouhar +2
As LLMs rapidly saturate existing benchmarks, automated benchmark creation using LLMs (LLM as a benchmark) where a model generates test inputs (LLM as a testset) and evaluates outp…
Enhancing Human Evaluation in Machine Translation with Comparative Judgment
Yixiao Song, Parker Riley, Daniel Deutsch +1
Human evaluation is crucial for assessing rapidly evolving language models but is influenced by annotator proficiency and task design. This study explores the integration of compar…
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects
Daniel Deutsch, Eleftheria Briakou, Isaac Caswell +14
As large language models (LLM) become more and more capable in languages other than English, it is important to collect benchmark datasets in order to evaluate their multilingual p…
Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data Contamination's Impact on Machine Translation
Muhammed Yusuf Kocyigit, Eleftheria Briakou, Daniel Deutsch +3
Data contamination -- the accidental consumption of evaluation examples within the pre-training data -- can undermine the validity of evaluation benchmarks. In this paper, we prese…