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This paper applies ROC analysis to evaluate automated translation quality estimation systems, offering a decision-oriented framework for assessing their performance in practical business scenarios. By demonstrating consistency with existing methods while providing actionable insights, the study advances the evaluation metrics critical for deploying reliable AI-driven localization tools. The focus remains strictly on improving the reliability and utility of machine learning models within computational linguistics.
Surse care acoperăarXiv, LessWrong, The Quantum Insider, AWS
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ROC Analysis for Evaluating Translation Quality Estimation Systems

arXiv1 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2605.24721v2 Announce Type: replace Abstract: The increasing use of automated translation quality estimation (QE) systems calls for practical, decision-oriented methods for evaluating their performance. We propose that Receiver Operating Characteristic (ROC) analysis is a useful approach for this purpose. Our study shows that ROC analysis not only produces results consistent with currently prevalent methods, but also offers several important advantages, including actionable performance insights that support business decision-making.

Abstract:The increasing use of automated translation quality estimation (QE) systems calls for practical, decision-oriented methods for evaluating their performance. We propose that Receiver Operating Characteristic (ROC) analysis is a useful approach for this purpose. Our study shows that ROC analysis not only produces results consistent with currently prevalent methods, but also offers several important advantages, including actionable performance insights that support business decision-making.

Computation and Language (cs.CL)

arXiv:2605.24721 [cs.CL]

(or arXiv:2605.24721v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.24721 Focus to learn more arXiv-issued DOI via DataCite

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