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Register a new userA Simple and Effective Positional Encoding for Transformers
ترميز وضعي بسيط وفعال للمحولات
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نماذج المحولات هي التقليب equivariant.لتزويد الطلب واكتب معلومات الرموز المميزة والإدخال، عادة ما تتم إضافتها إلى المدخلات.تعمل الأعمال الأخيرة الاختلافات المقترحة من الترميزات الموضعية مع ترميزات الموضع النسبي تحقيق أداء أفضل.يوضح تحليلنا أن المكسب يأتي في الواقع من نقل المعلومات الموضعية إلى طبقة الاهتمام من المدخلات.بدافع من ذلك، نقدم اهتماما ممتما مطردا للمحولات (النظام الغذائي)، وهي آلية بسيطة ولكنها فعالة لتشفير معلومات الموقف والقطاع في نماذج المحولات.تتمتع الطريقة المقترحة بتدريب ووقت الاستدلال بشكل أسرع، مع تحقيق أداء تنافسي في معايير الغراء وإكستريم و WMT.نحن نعتبر أكثر تعميم طريقتنا للمحولات الطويلة المدى وإظهار مكاسب الأداء.
Transformer models are permutation equivariant. To supply the order and type information of the input tokens, position and segment embeddings are usually added to the input. Recent works proposed variations of positional encodings with relative position encodings achieving better performance. Our analysis shows that the gain actually comes from moving positional information to attention layer from the input. Motivated by this, we introduce Decoupled Positional Attention for Transformers (DIET), a simple yet effective mechanism to encode position and segment information into the Transformer models. The proposed method has faster training and inference time, while achieving competitive performance on GLUE, XTREME and WMT benchmarks. We further generalize our method to long-range transformers and show performance gain.
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