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Register a new userThe Post-Editing Workflow: Training Challenges for LSPs, Post-Editors and Academia
سير العمل بعد التحرير: التحديات التدريبية ل LSPs، بعد المحررين والأوساط الأكاديمية
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تبنت تكنولوجيا اللغة بالفعل إلى حد كبير من قبل معظم مزودي خدمات اللغة (LSPs) ومدمج في عمليات الترجمة التقليدية. في هذا السياق، هناك العديد من الأساليب المختلفة لتطبيق النصوص بعد التحرير (PE) من نص مترجمة النص، بما في ذلك عمليات سير عمل مختلفة وخطوات يمكن أن تكون فعالة أكثر أو أقل فعالية ومواتية. في هذه الورقة، نقترح سير عمل بعد تحرير ثلاث خطوات (PEW). الرسم من Insight Insight، تهدف هذه الورقة إلى توفير إطار أساسي ل LSPs و Post-Editers حول كيفية تبسيط سير عمل ما بعد التحرير من أجل تحسين الجودة، وتحقيق ربحية أعلى وعودة أفضل على الاستثمار وتوحيد العمليات الداخلية من حيث جهود الإدارة واللغوية عندما يتعلق الأمر بخدمات PE. نقول أن PEW شامل يتكون في ثلاث مهام أساسية: عمليات تقييم ما قبل التحرير والتحرير بعد التحرير والترجمة التوضيحي (MT) (GUERRERO، 2018) المدعومة من ثلاثة أدوار أساسية: ما قبل المحرر، ما بعد المحرر والانجيلي ( جين، 2020). علاوة على ذلك، توضح الورقة المرسلة مسبقة التحديات التدريبية الناشئة عن هذه PEW، التي تدعمها نتائج البحوث التجريبية، على النحو الوارد في مسح رقمي بين المهنيين في مجال صناعة اللغة (الجينات، 2020)، التي أجريت في سياق ندوة الويب بعد التحرير وبعد تتألف عينة من 51 ممثلا لممثلي LSPs و 12 ممثلين عن ممثلي SLV (بائعي اللغة الفردي).
Language technology is already largely adopted by most Language Service Providers (LSPs) and integrated into their traditional translation processes. In this context, there are many different approaches to applying Post-Editing (PE) of a machine translated text, involving different workflow processes and steps that can be more or less effective and favorable. In the present paper, we propose a 3-step Post-Editing Workflow (PEW). Drawing from industry insight, this paper aims to provide a basic framework for LSPs and Post-Editors on how to streamline Post-Editing workflows in order to improve quality, achieve higher profitability and better return on investment and standardize and facilitate internal processes in terms of management and linguist effort when it comes to PE services. We argue that a comprehensive PEW consists in three essential tasks: Pre-Editing, Post-Editing and Annotation/Machine Translation (MT) evaluation processes (Guerrero, 2018) supported by three essential roles: Pre-Editor, Post-Editor and Annotator (Gene, 2020). Furthermore, the pre-sent paper demonstrates the training challenges arising from this PEW, supported by empirical research results, as reflected in a digital survey among language industry professionals (Gene, 2020), which was conducted in the context of a Post-Editing Webinar. Its sample comprised 51 representatives of LSPs and 12 representatives of SLVs (Single Language Vendors) representatives.
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Language technologies, such as machine translation (MT), but also the application of artificial intelligence in general and an abundance of CAT tools and platforms have an increasing influence on the translation market. Human interaction with these t
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