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Register a new userLearning Hard Retrieval Decoder Attention for Transformers
تعلم رموز فك التراجع الصعب الانتباه للمحولات
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يعتمد نموذج الترجمة المحول على آلية الاهتمام المتعدد الرأس، والتي يمكن توازتها بسهولة.تقوم شبكة الاهتمام المتعددة بالاهتمام بأداء وظيفة اهتمام المنتج DOT-Product المعزز بالتوازي، مما تمكن من تمكين النموذج من خلال حضور المعلومات المشتركة إلى معلومات من مختلف الفئات الفرعية التمثيلية في مواقف مختلفة.في هذه الورقة، نقدم نهجا لتعلم اهتمام استرجاع صعب حيث يحضر رأس الاهتمام فقط إلى رمز واحد في الجملة بدلا من جميع الرموز.وبالتالي، يمكن استبدال مضاعفة المصفوفة بين احتمالات الاهتمام وتسلسل القيمة في إيلاء اهتمام منتجات DOT-Product القياسية القياسية بعملية استرجاع بسيطة وفعالة.نظظ أن آلية اهتمام استرجاعها الثابت لدينا هي 1.43 مرة أسرع في فك التشفير، مع الحفاظ على جودة الترجمة على مجموعة واسعة من مهام الترجمة الآلية عند استخدامها في شبكات فك تشفير الذات والانتباه.
The Transformer translation model is based on the multi-head attention mechanism, which can be parallelized easily. The multi-head attention network performs the scaled dot-product attention function in parallel, empowering the model by jointly attending to information from different representation subspaces at different positions. In this paper, we present an approach to learning a hard retrieval attention where an attention head only attends to one token in the sentence rather than all tokens. The matrix multiplication between attention probabilities and the value sequence in the standard scaled dot-product attention can thus be replaced by a simple and efficient retrieval operation. We show that our hard retrieval attention mechanism is 1.43 times faster in decoding, while preserving translation quality on a wide range of machine translation tasks when used in the decoder self- and cross-attention networks.
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