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Register a new userSHAPE: Shifted Absolute Position Embedding for Transformers
الشكل: تحول الموقف المطلق إلى المحولات
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تمثيل الموقف أمر حاسم لبناء الممثلين على علم الموضع في المحولات.تعاني تمثيلات الموقف الموجودة من عدم وجود تعميم لاختبار البيانات بأطوال غير مرئية أو تكلفة حسابية عالية.نحقق التحقيق في تضمين الموقف المطلق (الشكل) لمعالجة كلا المشكلين.الفكرة الأساسية للشكل هي تحقيق التحول الثابتة، وهي ملكية رئيسية لتمثيلات الموقف الناجحة الأخيرة، من خلال تحويل المواقع المطلقة بشكل عشوائي أثناء التدريب.نوضح هذا الشكل مقارنة تجريبيا نظيره أثناء وجوده أبسط وأسرع.
Position representation is crucial for building position-aware representations in Transformers. Existing position representations suffer from a lack of generalization to test data with unseen lengths or high computational cost. We investigate shifted absolute position embedding (SHAPE) to address both issues. The basic idea of SHAPE is to achieve shift invariance, which is a key property of recent successful position representations, by randomly shifting absolute positions during training. We demonstrate that SHAPE is empirically comparable to its counterpart while being simpler and faster.
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