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Register a new userAugmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations
زيادة نماذج نمط بيرت مع ترميز تنبؤي لتحسين تمثيلات مستوى الخطاب
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عادة ما يتم تدريب نماذج اللغات الحالية على استخدام مخطط للإشراف على الذات، حيث يركز التركيز الرئيسي على التعلم في كلمة البرنامج أو مستوى الجملة.ومع ذلك، كان هناك تقدم محدود في توليد تمثيلات مفيدة على مستوى الخطاب.في هذا العمل، نقترح استخدام الأفكار من نظرية الترميز التنبؤية لزيادة نماذج اللغة ذات طراز بيرت مع آلية تسمح لهم بتعلم تمثيلات مناسبة على مستوى الخطاب.نتيجة لذلك، يكون نهجنا المقترح قادرا على التنبؤ بالأحكام المستقبلية باستخدام اتصالات واضحة من أعلى إلى أسفل تعمل في الطبقات المتوسطة للشبكة.من خلال تجربة معايير مصممة لتقييم المعرفة المتعلقة بالحبال باستخدام تمثيلات الجملة المدربة مسبقا، نوضح أن نهجنا يحسن الأداء في 6 من أصل 11 مهام من خلال التميز في كشف علاقة الخطاب.
Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level. However, there has been limited progress in generating useful discourse-level representations. In this work, we propose to use ideas from predictive coding theory to augment BERT-style language models with a mechanism that allows them to learn suitable discourse-level representations. As a result, our proposed approach is able to predict future sentences using explicit top-down connections that operate at the intermediate layers of the network. By experimenting with benchmarks designed to evaluate discourse-related knowledge using pre-trained sentence representations, we demonstrate that our approach improves performance in 6 out of 11 tasks by excelling in discourse relationship detection.
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