3 citations · 6 across the 19 of their papers we have counts for
19 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…
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…
From Jack of All Trades to Master of One: Specializing LLM-based Autoraters to a Test Set
Mara Finkelstein, Dan Deutsch, Parker Riley +3
As LLMs continue to become more powerful and versatile, human evaluation has quickly become intractable at scale and reliance on automatic metrics has become the norm. Recently, it…
Mitigating Metric Bias in Minimum Bayes Risk Decoding
Geza Kovacs, Daniel Deutsch, Markus Freitag
While Minimum Bayes Risk (MBR) decoding using metrics such as COMET or MetricX has outperformed traditional decoding methods such as greedy or beam search, it introduces a challeng…
Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data
Zhongtao Liu, Parker Riley, Daniel Deutsch +4
Collecting high-quality translations is crucial for the development and evaluation of machine translation systems. However, traditional human-only approaches are costly and slow. T…
MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task
Juraj Juraska, Daniel Deutsch, Mara Finkelstein +1
In this paper, we present the MetricX-24 submissions to the WMT24 Metrics Shared Task and provide details on the improvements we made over the previous version of MetricX. Our prim…
Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy
Brian Thompson, Nitika Mathur, Daniel Deutsch +1
Selecting an automatic metric that best emulates human annotators is often non-trivial, because there is no clear definition of "best emulates." A meta-metric is required to compar…
Finding Replicable Human Evaluations via Stable Ranking Probability
Parker Riley, Daniel Deutsch, George Foster +3
Reliable human evaluation is critical to the development of successful natural language generation models, but achieving it is notoriously difficult. Stability is a crucial require…
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…
LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback
Wenda Xu, Daniel Deutsch, Mara Finkelstein +6
Recent large language models (LLM) are leveraging human feedback to improve their generation quality. However, human feedback is costly to obtain, especially during inference. In t…
Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration
Daniel Deutsch, George Foster, Markus Freitag
Kendall's tau is frequently used to meta-evaluate how well machine translation (MT) evaluation metrics score individual translations. Its focus on pairwise score comparisons is int…