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This research introduces cESA, a novel human evaluation protocol for machine translation that assesses multiple outputs simultaneously to reduce annotator noise and cost. By leveraging shared context across several translations, the method enables more consistent absolute quality judgments and interpretable model rankings without complex post-hoc corrections. The study validates this approach using large-scale evaluations of English-to-Japanese translations from 12 different models, demonstrating significant efficiency gains over standard pointwise evaluation methods.
Contrastive ESA: Human Evaluation of Multiple Translations at Once
arXiv:2607.26640v1 Announce Type: new Abstract: Current human evaluation of machine translation typically assesses single outputs in isolation, a paradigm that suffers from high annotator noise and cost. We introduce Contrastive Error Span Annotation (cESA), a protocol that presents multiple translations of the source input (text, video, audio, image). In cESA, the annotator sees multiple translations of the same document, marks major and minor error spans, and then assigns a score from 0% to 100% on absolute scale. By allowing annotators to access the shared context across multiple outputs, cESA facilitates more consistent and efficient judgments. We validate cESA using a large-scale human evaluation of English->Japanese translations of 12 models, demonstrating reductions in annotation time and noise compared to standard pointwise evaluation. Unlike existing contrastive ranking methods, cESA yields absolute quality judgments that enable simple, interpretable non-parametric model rankings without the need for post-hoc corrections.
Abstract:Current human evaluation of machine translation typically assesses single outputs in isolation, a paradigm that suffers from high annotator noise and cost. We introduce Contrastive Error Span Annotation (cESA), a protocol that presents multiple translations of the source input (text, video, audio, image). In cESA, the annotator sees multiple translations of the same document, marks major and minor error spans, and then assigns a score from 0% to 100% on absolute scale. By allowing annotators to access the shared context across multiple outputs, cESA facilitates more consistent and efficient judgments. We validate cESA using a large-scale human evaluation of English->Japanese translations of 12 models, demonstrating reductions in annotation time and noise compared to standard pointwise evaluation. Unlike existing contrastive ranking methods, cESA yields absolute quality judgments that enable simple, interpretable non-parametric model rankings without the need for post-hoc corrections.
Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
arXiv:2607.26640 [cs.CL]
(or arXiv:2607.26640v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.26640 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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