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Register a new userExploring the Importance of Source Text in Automatic Post-Editing for Context-Aware Machine Translation
استكشاف أهمية النص المصدر في التحرير التلقائي للترجمة الآلية للترجمة الآلية
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تتطلب الترجمة الدقيقة معلومات على مستوى المستندات، والتي يتم تجاهلها بواسطة الترجمة الآلية على مستوى الجملة.لقد أظهر العمل الحديث أن الاتساق على مستوى المستند يمكن تحسينه باستخدام معلومات ما بعد التحرير التلقائي باستخدام معلومات اللغة المستهدفة فقط (TL).ندرس نموذج قرد موسع يدمج سياق المصدر.يكشف التقييم البشري للطلاقة والكفاة باللغة الإنجليزية - الترجمة الروسية الإنجليزية أن النموذج الذي يتمتع بالوصول إلى سياق المصدر يتفوق بشكل كبير على قرد أحادي الأحادي من حيث الكفاية، وهو تأثير تجاهله إلى حد كبير بواسطة مقاييس التقييم التلقائي إلى حد كبير.تظهر نتائجنا أن نمذجة TL فقط يزيد الطلاقة دون تحسين كفاية، مما يدل على الحاجة إلى تكييف النص المصدر لتحرير ما بعد التحرير التلقائي.كما أنها تسليط الضوء على النقاط العمياء في الأساليب التلقائية للتقييم المستهدف وإظهار الحاجة إلى تقييم بشري لتقييم جودة الترجمة على مستوى المستند بشكل موثوق.
Accurate translation requires document-level information, which is ignored by sentence-level machine translation. Recent work has demonstrated that document-level consistency can be improved with automatic post-editing (APE) using only target-language (TL) information. We study an extended APE model that additionally integrates source context. A human evaluation of fluency and adequacy in English--Russian translation reveals that the model with access to source context significantly outperforms monolingual APE in terms of adequacy, an effect largely ignored by automatic evaluation metrics. Our results show that TL-only modelling increases fluency without improving adequacy, demonstrating the need for conditioning on source text for automatic post-editing. They also highlight blind spots in automatic methods for targeted evaluation and demonstrate the need for human assessment to evaluate document-level translation quality reliably.
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