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Register a new userSequence-to-Lattice Models for Fast Translation
نماذج تسلسل إلى شعرية للترجمة السريعة
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نهج الترجمة الآلية غير التلقائية (NAT) تتيح الجيل السريع عن طريق الاستفادة من العمليات الاسرد الاشتراكية.عنق الزجاجة المتبقية في هذه النماذج هي طبقات فك التشفير الخاصة بهم؛لسوء الحظ على عكس النماذج التلقائية (Kasai et al.، 2020)، إزالة طبقات فك ترميز من نماذج NAT تتحلل بشكل كبير الدقة.يقترح هذا العمل نموذجا تسلسل إلى شعرية يحل محل وحدة فك التشفير مع شعرية البحث.تقوم نهجنا أولا بإنشاء شعرية مرشح باستخدام عمليات البحث الفعالة، ويولد درجات شعرية من تشفير عميق، وأخيرا يجد أفضل المسار باستخدام البرمجة الديناميكية.تظهر التجارب على ثلاث مجموعات بيانات الترجمة الآلية أن طريقتنا أسرع من نهج الجيل غير التلقائي الماضي غير الدقيق، وأكثر دقة من الحد السامي من عدد طبقات فك التشفير.
Non-autoregressive machine translation (NAT) approaches enable fast generation by utilizing parallelizable generative processes. The remaining bottleneck in these models is their decoder layers; unfortunately unlike in autoregressive models (Kasai et al., 2020), removing decoder layers from NAT models significantly degrades accuracy. This work proposes a sequence-to-lattice model that replaces the decoder with a search lattice. Our approach first constructs a candidate lattice using efficient lookup operations, generates lattice scores from a deep encoder, and finally finds the best path using dynamic programming. Experiments on three machine translation datasets show that our method is faster than past non-autoregressive generation approaches, and more accurate than naively reducing the number of decoder layers.
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