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Machine translation post-editing (MTPE) is a process where humans and machines meet. While previous researchers have adopted psychological and cognitive approaches to explore the factors affecting MTPE performance, little research has been carried out to simultaneously investigate the post-editors’ cognitive traits and the post-editing task properties. This paper addresses this gap by focusing on perceived post-editing self-efficacy (PESE) as a key cognitive trait. By adopting mixed methods of keylogging, screen-recording, and subjective rating, this paper attempts to empirically assess the effects of student translators’ PESE and machine translation (MT) quality on their cognitive effort and post-edited quality. Data were obtained from 106 Chinese student translators concerning cognitive effort (indicated by processing time per word, pause density, pause duration per word, and perceived cognitive effort) of post-editing tasks and the post-edited quality (indicated by average accuracy score and average fluency score). Results show that MT quality significantly influences both the process and product within a PE task. PESE has effects on participants’ perceived cognitive effort and post-edited quality, but not on actual cognitive effort. No significant interaction effect of MT quality and PESE on PE performance was observed.

En aquest article descrivim un instrument de diagnòstic per avaluar la pràctica de la post-edició. Tot i que hi ha exemples d'aquests instruments a l'abast, rara vegada es fan servir els estudis empírics com a base per a avaluar-lo. Esperem que la nostra eina pugui ajudar a seleccionar professionals de la traducció o alumnat de traducció adequats per a projectes de post-edició amb la detecció dels coneixements, les competències o les actituds importants per fer aquesta feina i que fins ara  faltaven en el comportament dels aspirants a fer-la. In the present article, we provide an outline of a diagnostic tool for testing post-editing performance. The tool is hoped to facilitate the identification of suitable translators/translation students for post-editing projects by flagging knowledge, skills or attitudes relevant to post-editing that is/are found to be lacking in candidates' post-editing behaviour. En el presente artículo perfilamos una herramienta de diagnóstico para evaluar la práctica de la posedición. No es habitual que se utilicen estudios empíricos como base para su evaluación, a pesar de disponer de ejemplos de tales instrumentos. Esperamos que nuestra herramienta ayude a seleccionar profesionales de la traducción o alumnado de traducción adecuado para proyectos de posedición con la detección de conocimientos, competencias o  actitudes importantes para este trabajo que no se encontraban hasta ahora en el comportamiento de los aspirantes a desempeñarlo.

Medical machine translation (MT) post-editing faces significant challenges regarding insufficient targeting and poor adaptability to long texts. To address this, this study proposes a hierarchical post-editing strategy integrating the Dynamic Quality Framework (DQF) and Multidimensional Quality Metrics (MQM). Unlike traditional passive correction methods, this study introduces a proactive closed-loop model featuring ‘identification, risk assessment, and control’. We utilize medical clinical reports to construct an error priority model and a propagation risk mechanism based on a BERT-Knowledge Graph architecture. Experimental results demonstrate that this strategy improves editing efficiency by 41.26% (p < 0.05), achieves an average MT quality score of 89.17, and attains F1 scores of 93.85% and 89.26% for terminology and semantic error recognition, respectively. Notably, in 5,000-word texts, editing time was reduced by over 45%. The strategy shows high robustness, with manual intervention increasing by only 0.8–1.2% for every 0.5‰ rise in term ambiguity density. This study provides a reproducible, risk-controllable paradigm for high-stakes medical translation, balancing quality and efficiency.

Cochrane produces independent research to improve healthcare decisions. It translates its research summaries into different languages to enable wider access, relying largely on volunteers. Machine translation (MT) could facilitate efficiency in Cochrane’s low-resource environment. We compared three off-the-shelf machine translation engines (MTEs)—DeepL, Google Translate and Microsoft Translator—for Russian translations of Cochrane plain language summaries (PLSs) by assessing the quantitative human post-editing effort within an established translation workflow and quality assurance process. 30 PLSs each were pre-translated with one of the three MTEs. Ten volunteer translators post-edited nine randomly assigned PLSs each—three per MTE—in their usual translation system, Memsource. Two editors performed a second editing step. Memsource’s Machine Translation Quality Estimation (MTQE) feature provided an artificial intelligence (AI)-powered estimate of how much editing would be required for each PLS, and the analysis feature calculated the amount of human editing after each editing step. Google Translate performed the best with highest average quality estimates for its initial MT output, and the lowest amount of human post-editing. DeepL performed slightly worse, and Microsoft Translator worst. Future developments in MT research and the associated industry may change our results.