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مشاركة HW-TSC في المهمة المشتركة الكفاءة WMT 2021
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تقدم هذه الورقة تقديم مركز خدمات الترجمة Huawei (HW-TSC) إلى مهمة مشتركة من WMT 2021.نستكشف تقنية تقطير الطالبات على مستوى الجملة وتدريب العديد من النماذج الصغيرة التي تجد التوازن بين الكفاءة والجودة.تتميز نماذجنا بمثابة تشفير عميق ومكتشف ضحل وخفيف الوزن RNN مع طبقة SSRU.نستخدم بولت هواوي نوح، مكتبة فعالة ووزن خفيفة للاستدلال على الجهاز.الاستفادة من كمية INT8 الكمي، مشغل مصفوفة العام (GEMM) العام المعرفة ذاتيا (GEMM)، القائمة المختصرة، البحث الجشع والتخزين المؤقت، نقدم أربعة نماذج ترجمة صغيرة الحجم وكفاءة مع جودة الترجمة عالية لمسار الكمون CPU واحد.
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2021 Efficiency Shared Task. We explore the sentence-level teacher-student distillation technique and train several small-size models that find a balance between efficiency and quality. Our models feature deep encoder, shallow decoder and light-weight RNN with SSRU layer. We use Huawei Noah's Bolt, an efficient and light-weight library for on-device inference. Leveraging INT8 quantization, self-defined General Matrix Multiplication (GEMM) operator, shortlist, greedy search and caching, we submit four small-size and efficient translation models with high translation quality for the one CPU core latency track.
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