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Register a new userExplore Better Relative Position Embeddings from Encoding Perspective for Transformer Models
استكشاف أفضل تضمين المنافذ النسبي من منظور الترميز لنماذج المحولات
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إن تضمين الموضع النسبي (RPE) هو طريقة ناجحة لتشفير معلومات موقف مركزية وفعالة في نماذج المحولات.في هذه الورقة، نحقق في المشكلات المحتملة في Shaw-RPE و XL-RPE، والتي تعد أكثر من الممثلين والجلوب السائدة، واقتراح اثنين من روبيس رواية تسمى RPE الخشنة الخشنة الرفيعة المستوى الرفيع المستوى (LFHC)Gaussian وظيفة التوزيع التراكمي (GCDF) RPE.LFHC-RPE هو تحسن شو-RPE، مما يعزز قدرة التصور على المناصب النسبية المتوسطة والطويلة.تستخدم GCDF-RPE الخصائص الممتازة لوظيفة Gaussian لتعديل آلية الترميز السابقة في XL-RPE.النتائج التجريبية على تسعة مجموعات بيانات موثوقة تظهر فعالية أساليبنا تجريبيا.علاوة على ذلك، تحقق GCDF-RPE أفضل الأداء العام بين خمسة RPES مختلفة.
Relative position embedding (RPE) is a successful method to explicitly and efficaciously encode position information into Transformer models. In this paper, we investigate the potential problems in Shaw-RPE and XL-RPE, which are the most representative and prevalent RPEs, and propose two novel RPEs called Low-level Fine-grained High-level Coarse-grained (LFHC) RPE and Gaussian Cumulative Distribution Function (GCDF) RPE. LFHC-RPE is an improvement of Shaw-RPE, which enhances the perception ability at medium and long relative positions. GCDF-RPE utilizes the excellent properties of the Gaussian function to amend the prior encoding mechanism in XL-RPE. Experimental results on nine authoritative datasets demonstrate the effectiveness of our methods empirically. Furthermore, GCDF-RPE achieves the best overall performance among five different RPEs.
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