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khalilizadeh Ganjalikhani, Marziyeh. (1405). Investigating Machine Translation Post-Editing Outcomes in Expert-Based Closed Crowdsourcing: Evidence from a Qualitative Study. نشریه علمی دانشگاه سیستان و بلوچستان, (), -. doi: 10.22111/ijals.2026.52165.2526
Marziyeh khalilizadeh Ganjalikhani. "Investigating Machine Translation Post-Editing Outcomes in Expert-Based Closed Crowdsourcing: Evidence from a Qualitative Study". نشریه علمی دانشگاه سیستان و بلوچستان, , , 1405, -. doi: 10.22111/ijals.2026.52165.2526
khalilizadeh Ganjalikhani, Marziyeh. (1405). 'Investigating Machine Translation Post-Editing Outcomes in Expert-Based Closed Crowdsourcing: Evidence from a Qualitative Study', نشریه علمی دانشگاه سیستان و بلوچستان, (), pp. -. doi: 10.22111/ijals.2026.52165.2526
khalilizadeh Ganjalikhani, Marziyeh. Investigating Machine Translation Post-Editing Outcomes in Expert-Based Closed Crowdsourcing: Evidence from a Qualitative Study. نشریه علمی دانشگاه سیستان و بلوچستان, 1405; (): -. doi: 10.22111/ijals.2026.52165.2526

Investigating Machine Translation Post-Editing Outcomes in Expert-Based Closed Crowdsourcing: Evidence from a Qualitative Study

Iranian Journal of Applied Language Studies
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 11 تیر 1405
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijals.2026.52165.2526
نویسنده
Marziyeh khalilizadeh Ganjalikhani*
Foreign Languages Department, Faculty of Tourism, Higher Education Complex of Bam, Bam, Kerman, Iran
چکیده
Post-editing refers to activities aimed at improving machine translation (MT) output to achieve human-level quality. Crowdsourcing translation post-editing (CTPE) can provide a cost-effective approach, yet open crowdsourcing models often suffer from inconsistent quality due to varying participant expertise. This study examines a closed, expert-driven crowdsourcing model for Persian health translations, using a qualitative approach to analyze post-editing practices within a Telegram-based expert community. Thirty qualified translators performed post-editing on machine-generated translations of health-related texts, with three independent expert proofreaders evaluating multiple candidate outputs per instance. Problematic instances were identified and assessed according to linguistic criteria adapted from the Patient-Oriented and Culturally Appropriate (POCA) health translation framework, focusing on clarity, coherence, lexical appropriateness, and readability. Findings indicate that this closed model facilitates careful, context-sensitive post-editing, producing multiple acceptable solutions per instance, and may tentatively reduce the need for extensive proofreading. These results underscore the potential of structured, expert-driven CTPE for supporting translation quality in specialized domains while highlighting the interpretive complexity inherent in post-editing tasks.
کلیدواژه‌ها
Closed Crowdsourcing؛ Expert-driven Crowdsourcing؛ Machine Translation؛ Post-editing؛ Translation Quality
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تعداد مشاهده مقاله: 13


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