Proposes STEL task with protocol and dataset; shows XCOMET and Qwen2.5-Omni label errors at roughly half human precision and that speech processing is required.
Pearmut: Human Evaluation of Translation Made Trivial
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Human evaluation is the gold standard for multilingual NLP, but is often skipped in practice and substituted with automatic metrics because it is notoriously complex and slow to set up with existing tools with substantial engineering and operational overhead. We introduce Pearmut, a lightweight yet feature-rich platform that makes end-to-end human evaluation as easy to run as automatic evaluation. Pearmut removes common entry barriers and provides support for evaluating multilingual tasks, with a particular focus on machine translation. The platform implements standard evaluation protocols, including DA, ESA, and MQM, and is extensible to support new protocols. It features document-level context, absolute and contrastive evaluation, attention checks, ESAAI pre-annotations and both static and dynamic assignment strategies. Pearmut enables reliable human evaluation to become a practical, routine component of model development and diagnosis rather than an occasional effort.
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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Automatic Labelling of Speech Translation Errors
Proposes STEL task with protocol and dataset; shows XCOMET and Qwen2.5-Omni label errors at roughly half human precision and that speech processing is required.