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Register a new userEncouraging Lexical Translation Consistency for Document-Level Neural Machine Translation
تشجيع تناسق الترجمة المعجمية للترجمة الآلية النموذجية على مستوى المستند
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في الآونة الأخيرة، تم اقتراح عدد من الأساليب لتحسين أداء الترجمة للترجمة الآلية العصبية على مستوى المستند (NMT). ومع ذلك، فإن القليل من التركيز على موضوع تناسق الترجمة المعجمية. في هذه الورقة، نطبق ترجمة واحدة لكل خطاب "في NMT، وتهدف إلى تشجيع تناسق الترجمة المعجمية ل NMT على مستوى المستند. تتم ثم نشجع ترجمة هذه الكلمات داخل رابط لتكون متسقة بطريقتين. من ناحية، عند ترميز الجمل داخل وثيقة نتخذها بشكل صحيح معلومات السياق من هذه الكلمات. من ناحية أخرى، نقترح وظيفة خسارة مساعدة إلى تقييد أفضل أن ترجمتهم يجب أن تكون متسقة. النتائج التجريبية على الصينية english والإنجليزية → توضح مهام الترجمة الفرنسية أن نهجنا لا يحقق فقط الأداء الحديث في درجات بلو، ولكن أيضا يحسن إلى حد كبير الاتساق المعجمي في الترجمة.
Recently a number of approaches have been proposed to improve translation performance for document-level neural machine translation (NMT). However, few are focusing on the subject of lexical translation consistency. In this paper we apply one translation per discourse'' in NMT, and aim to encourage lexical translation consistency for document-level NMT. This is done by first obtaining a word link for each source word in a document, which tells the positions where the source word appears. Then we encourage the translation of those words within a link to be consistent in two ways. On the one hand, when encoding sentences within a document we properly share context information of those words. On the other hand, we propose an auxiliary loss function to better constrain that their translation should be consistent. Experimental results on Chinese↔English and English→French translation tasks show that our approach not only achieves state-of-the-art performance in BLEU scores, but also greatly improves lexical consistency in translation.
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