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Register a new userAdapting Neural Machine Translation for Automatic Post-Editing
تكييف الترجمة الآلية العصبية للتوتدي بعد التحرير
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يتم استخدام نماذج ما بعد التحرير التلقائي (APE) مخرجات نظام الترجمة الآلية (MT) الصحيحة عن طريق التعلم من أنماط ما بعد التحرير البشري.نقدم النظام المستخدم في التقديم الخاص بنا إلى المهمة المشتركة (APE) APانية (EN-DE).نستفيد نظام MT الحديث (NG et al.، 2019) لهذه المهمة.للحصول على مزيد من التحسينات، نقوم بتكييف نموذج MT إلى مجال المهام باستخدام Wikimatrix (Schwenket al.، 2021) متبوعا بضبط جيد مع عينات إضافية للقرد من الإصدارات السابقة للمهمة المشتركة (WMT-16،17،18) وتمتلكنماذج.تغلب أنظمتنا على خط الأساس على درجات TER على مجموعة اختبار WMT'21.
Automatic post-editing (APE) models are usedto correct machine translation (MT) system outputs by learning from human post-editing patterns. We present the system used in our submission to the WMT'21 Automatic Post-Editing (APE) English-German (En-De) shared task. We leverage the state-of-the-art MT system (Ng et al., 2019) for this task. For further improvements, we adapt the MT model to the task domain by using WikiMatrix (Schwenket al., 2021) followed by fine-tuning with additional APE samples from previous editions of the shared task (WMT-16,17,18) and ensembling the models. Our systems beat the baseline on TER scores on the WMT'21 test set.
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