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Register a new userContrastive Document Representation Learning with Graph Attention Networks
تمثيل وثيقة مناقصة التعلم مع شبكات انتباه الرسوم البيانية
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أظهرت التقدم المحرز الأخير في نماذج اللغة المستندة إلى المحولات الاحترام نجاحا كبيرا في تعلم التمثيل السياقي للنص.ومع ذلك، نظرا لتعقيد الاهتمام من الدرجة الثانية، يمكن لمعظم نماذج المحولات مسبقا التعامل مع النص القصير نسبيا.لا يزال يمثل تحديا عندما يتعلق الأمر بنمذة مستندات طويلة جدا.في هذا العمل، نقترح استخدام شبكة انتباه الرسوم البيانية أعلى نموذج المحولات مسبقا متوفرة لتعلم تضمين الوثائق.تتيح لنا شبكة انتباه الرسم البياني هذه الاستفادة من الهيكل الدلالي الرفيع المستوى للوثيقة.بالإضافة إلى ذلك، استنادا إلى نموذج وثيقة الرسم البياني لدينا، نقوم بتصميم استراتيجية تعليمية بسيطة بسيطة للتعبير عن نماذجنا بمقدار كبير من الكائنات الكبيرة.تجريبيا، نوضح فعالية نهجنا في تصنيف الوثائق ومهام استرجاع المستندات.
Recent progress in pretrained Transformer-based language models has shown great success in learning contextual representation of text. However, due to the quadratic self-attention complexity, most of the pretrained Transformers models can only handle relatively short text. It is still a challenge when it comes to modeling very long documents. In this work, we propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings. This graph attention network allows us to leverage the high-level semantic structure of the document. In addition, based on our graph document model, we design a simple contrastive learning strategy to pretrain our models on a large amount of unlabeled corpus. Empirically, we demonstrate the effectiveness of our approaches in document classification and document retrieval tasks.
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