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Register a new userTag Assisted Neural Machine Translation of Film Subtitles
العلامة بمساعدة آلة الجهاز العصبي ترجمات الأفلام
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نفذنا نظام ترجمة آلات عصبية يستخدم علامات التسلسل التلقائي لتحسين جودة الترجمة.بدلا من التشغيل في أزواج الجملة غير المركبة، يستخدم نظامنا أنظمة العلامات المدربة مسبقا لإضافة ميزات لغوية إلى المصدر الجمل المستهدفة.تتعلم بنية العصبية المقترحة لدينا تضمين مشترك للرموز والعلامات في التشفير، والرمز المتزامن والتنبؤ بالعلامة في وحدة فك التشفير.بالمقارنة مع خط الأساس مع التدريب غير المرفقي، زادت هذه الهيكل هذه النتيجة بلو للألمانية إلى إخراج الترجمة الفرعية للفيلم الإنجليزي بمقدار 1.61 نقطة باستخدام علامات الكيان المسماة؛ومع ذلك، انخفضت النتيجة بلو بمقدار 0.38 نقطة باستخدام علامات جزء من الكلام.وهذا يدل على أن مخرجات العلامات على مستوى الرمز المميز من أنظمة العلامات خارج الرف يمكن أن تحسن إخراج أنظمة الترجمة العصبية باستخدام التضمين المشترك ومجموعات فك التشفير المتزامنة.
We implemented a neural machine translation system that uses automatic sequence tagging to improve the quality of translation. Instead of operating on unannotated sentence pairs, our system uses pre-trained tagging systems to add linguistic features to source and target sentences. Our proposed neural architecture learns a combined embedding of tokens and tags in the encoder, and simultaneous token and tag prediction in the decoder. Compared to a baseline with unannotated training, this architecture increased the BLEU score of German to English film subtitle translation outputs by 1.61 points using named entity tags; however, the BLEU score decreased by 0.38 points using part-of-speech tags. This demonstrates that certain token-level tag outputs from off-the-shelf tagging systems can improve the output of neural translation systems using our combined embedding and simultaneous decoding extensions.
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