@inproceedings{zouhar-etal-2026-contrastive,
title = "Contrastive {ESA}: Human Evaluation of Multiple Translations at Once",
author = "Zouhar, Vilem and
Grundkiewicz, Roman and
Rajaee, Sara and
Riley, Parker and
Bawden, Rachel and
Koehn, Philipp and
Carpuat, Marine and
Kocmi, Tom",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.47/",
pages = "860--872",
ISBN = "979-8-89176-474-3",
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."
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<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>
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%0 Conference Proceedings
%T Contrastive ESA: Human Evaluation of Multiple Translations at Once
%A Zouhar, Vilem
%A Grundkiewicz, Roman
%A Rajaee, Sara
%A Riley, Parker
%A Bawden, Rachel
%A Koehn, Philipp
%A Carpuat, Marine
%A Kocmi, Tom
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F zouhar-etal-2026-contrastive
%X 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.
%U https://aclanthology.org/2026.wmt-main.47/
%P 860-872
Markdown (Informal)
[Contrastive ESA: Human Evaluation of Multiple Translations at Once](https://aclanthology.org/2026.wmt-main.47/) (Zouhar et al., WMT 2026)
ACL
- Vilem Zouhar, Roman Grundkiewicz, Sara Rajaee, Parker Riley, Rachel Bawden, Philipp Koehn, Marine Carpuat, and Tom Kocmi. 2026. Contrastive ESA: Human Evaluation of Multiple Translations at Once. In Proceedings of the Eleventh Conference on Machine Translation, pages 860–872, Budapest, Hungary. Association for Computational Linguistics.