@inproceedings{kocmi-etal-2026-findings,
title = "Findings of the {WMT}26 General Machine Translation Shared Task: Contrastive Dynamic Human Evaluation at Scale",
author = "Kocmi, Tom and
Artemova, Ekaterina and
Avramidis, Eleftherios and
Bawden, Rachel and
Bojar, Ond{\v{r}}ej and
Dukanov, Sergey and
Dvorkovich, Anton and
Fishel, Mark and
Freitag, Markus and
Frontull, Samuel and
Gowda, Thamme and
Grundkiewicz, Roman and
Haddow, Barry and
Kharevich, Stan and
Koehn, Philipp and
Li, Zheng and
Maillard, Jean and
Monz, Christof and
Murauski, Alexander and
Murray, Kenton and
Nagata, Masaaki and
Perrella, Stefano and
Popel, Martin and
Popovi{\'c}, Maja and
Proietti, Lorenzo and
Rajaee, Sara and
Riley, Parker and
Shmatova, Mariya and
Steingr{\'i}msson, Steinth{\'o}r and
Yankovskaya, Lisa and
Zouhar, Vilem",
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.48/",
pages = "873--923",
ISBN = "979-8-89176-474-3",
abstract = "This paper presents the results of the General Machine Translation Task organized under the 2026 Conference on Machine Translation (WMT). Participants build systems for any of the 23 language pairs spanning four to five domains. This year we brought major changes to the human evaluation: (1) new annotation platform Pearmut for better reproducibility, (2) new human protocol ``contrastive Error Span Annotation'' for higher quality and side-by-side evaluation, and (3) Dynamic human evaluation that allows assessing all submitted models while evaluating higher-performing models more frequently. Beyond human evaluation, we (4) extended difficulty sampling with a human-driven stage, (5) added two new domains, (6) made the test sets fully document-level without requiring segment-level alignment and relying on HTML or JSON structure, (7) prepared some human references by post-editing open-weight model outputs, and (8) introduced contextual instructions governing formality and structural style. We evaluated 46 systems in total: 30 submitted by participants and 16 consisting of translations from large language models (LLMs) and industry translation providers."
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<abstract>This paper presents the results of the General Machine Translation Task organized under the 2026 Conference on Machine Translation (WMT). Participants build systems for any of the 23 language pairs spanning four to five domains. This year we brought major changes to the human evaluation: (1) new annotation platform Pearmut for better reproducibility, (2) new human protocol “contrastive Error Span Annotation” for higher quality and side-by-side evaluation, and (3) Dynamic human evaluation that allows assessing all submitted models while evaluating higher-performing models more frequently. Beyond human evaluation, we (4) extended difficulty sampling with a human-driven stage, (5) added two new domains, (6) made the test sets fully document-level without requiring segment-level alignment and relying on HTML or JSON structure, (7) prepared some human references by post-editing open-weight model outputs, and (8) introduced contextual instructions governing formality and structural style. We evaluated 46 systems in total: 30 submitted by participants and 16 consisting of translations from large language models (LLMs) and industry translation providers.</abstract>
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%0 Conference Proceedings
%T Findings of the WMT26 General Machine Translation Shared Task: Contrastive Dynamic Human Evaluation at Scale
%A Kocmi, Tom
%A Artemova, Ekaterina
%A Avramidis, Eleftherios
%A Bawden, Rachel
%A Bojar, Ondřej
%A Dukanov, Sergey
%A Dvorkovich, Anton
%A Fishel, Mark
%A Freitag, Markus
%A Frontull, Samuel
%A Gowda, Thamme
%A Grundkiewicz, Roman
%A Haddow, Barry
%A Kharevich, Stan
%A Koehn, Philipp
%A Li, Zheng
%A Maillard, Jean
%A Monz, Christof
%A Murauski, Alexander
%A Murray, Kenton
%A Nagata, Masaaki
%A Perrella, Stefano
%A Popel, Martin
%A Popović, Maja
%A Proietti, Lorenzo
%A Rajaee, Sara
%A Riley, Parker
%A Shmatova, Mariya
%A Steingrímsson, Steinthór
%A Yankovskaya, Lisa
%A Zouhar, Vilem
%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 kocmi-etal-2026-findings
%X This paper presents the results of the General Machine Translation Task organized under the 2026 Conference on Machine Translation (WMT). Participants build systems for any of the 23 language pairs spanning four to five domains. This year we brought major changes to the human evaluation: (1) new annotation platform Pearmut for better reproducibility, (2) new human protocol “contrastive Error Span Annotation” for higher quality and side-by-side evaluation, and (3) Dynamic human evaluation that allows assessing all submitted models while evaluating higher-performing models more frequently. Beyond human evaluation, we (4) extended difficulty sampling with a human-driven stage, (5) added two new domains, (6) made the test sets fully document-level without requiring segment-level alignment and relying on HTML or JSON structure, (7) prepared some human references by post-editing open-weight model outputs, and (8) introduced contextual instructions governing formality and structural style. We evaluated 46 systems in total: 30 submitted by participants and 16 consisting of translations from large language models (LLMs) and industry translation providers.
%U https://aclanthology.org/2026.wmt-main.48/
%P 873-923
Markdown (Informal)
[Findings of the WMT26 General Machine Translation Shared Task: Contrastive Dynamic Human Evaluation at Scale](https://aclanthology.org/2026.wmt-main.48/) (Kocmi et al., WMT 2026)
ACL
- Tom Kocmi, Ekaterina Artemova, Eleftherios Avramidis, Rachel Bawden, Ondřej Bojar, Sergey Dukanov, Anton Dvorkovich, Mark Fishel, Markus Freitag, Samuel Frontull, Thamme Gowda, Roman Grundkiewicz, Barry Haddow, Stan Kharevich, Philipp Koehn, Zheng Li, Jean Maillard, Christof Monz, Alexander Murauski, Kenton Murray, Masaaki Nagata, Stefano Perrella, Martin Popel, Maja Popović, Lorenzo Proietti, Sara Rajaee, Parker Riley, Mariya Shmatova, Steinthór Steingrímsson, Lisa Yankovskaya, and Vilem Zouhar. 2026. Findings of the WMT26 General Machine Translation Shared Task: Contrastive Dynamic Human Evaluation at Scale. In Proceedings of the Eleventh Conference on Machine Translation, pages 873–923, Budapest, Hungary. Association for Computational Linguistics.