APEX-VW: A Document-Level English-Spanish Post-Editing Dataset in the Healthcare Domain
- URL: http://arxiv.org/abs/2608.08059v1
- Date: Sat, 08 Aug 2026 10:54:44 GMT
- Title: APEX-VW: A Document-Level English-Spanish Post-Editing Dataset in the Healthcare Domain
- Abstract summary: Post-Editing (PE) of Machine Translation (MT) output often involves repeating the same lexical and terminological corrections across many segments.<n>This paper presents the APEX-VW Corpus, a new open English-Spanish dataset built from recent NHS virtual-ward documents and professional translation in Trados Studio.<n>Unlike prior resources such as WMT APE corpora, eSCAPE, MLQE-PE, or LangMark, the dataset preserves document order and CAT-tool context.
- Score: 7.333648670424977
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Post-Editing (PE) of Machine Translation (MT) output often involves repeating the same lexical and terminological corrections across many segments, especially in specialised and highly repetitive documents. Despite substantial work on Automatic Post-Editing (APE), most available corpora operate at the sentence level, others are synthetic, and overall not designed to study how corrections propagate in realistic Computer-Assisted Translation (CAT) workflows. This paper presents the APEX-VW (Automatic Post-Editing eXperiments on Virtual Wards) Corpus, a new open English-Spanish (EN-ES) dataset built from recent NHS virtual-ward documents and professional PE in Trados Studio, with controlled MT, terminology, and quality assurance settings. The corpus contains seven document-coherent source texts totalling 42k words, translated with four MT systems representing different paradigms and then post-edited by professional translators. Unlike prior resources such as WMT APE corpora, eSCAPE, MLQE-PE, or LangMark, the dataset preserves document order and CAT-tool context, making it suitable for research on terminology normalisation, correction propagation, and human-in-the-loop translation support. The paper describes the corpus design, data preparation, PE setup, and initial corpus statistics, and positions the resource as a benchmark for document-level APE and propagation-aware assistive tools.
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