4 citations · 10 across the 23 of their papers we have counts for
23 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…
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…
Penalizing Length: Uncovering Systematic Bias in Quality Estimation Metrics
Yilin Zhang, Wenda Xu, Zhongtao Liu +2
Quality Estimation (QE) metrics are vital in machine translation for reference-free evaluation and increasingly serve as selection criteria in data filtering and candidate rerankin…
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…
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…
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…
Learning from others' mistakes: Finetuning machine translation models with span-level error annotations
Lily H. Zhang, Hamid Dadkhahi, Mara Finkelstein +3
Despite growing interest in incorporating feedback to improve language models, most efforts focus only on sequence-level annotations. In this work, we explore the potential of util…
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…
Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data
Mara Finkelstein, David Vilar, Markus Freitag
Recent research in neural machine translation (NMT) has shown that training on high-quality machine-generated data can outperform training on human-generated data. This work accomp…
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
Firas Trabelsi, David Vilar, Mara Finkelstein +1
Minimum Bayes Risk (MBR) decoding is a powerful decoding strategy widely used for text generation tasks, but its quadratic computational complexity limits its practical application…
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…
Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model
Christian Tomani, David Vilar, Markus Freitag +5
Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy for neural machine translation (NMT) models. The underlying assumption is that model probability corre…
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…
INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback
Wenda Xu, Danqing Wang, Liangming Pan +4
Automatically evaluating the quality of language generation is critical. Although recent learned metrics show high correlation with human judgement, these metrics can not explain t…