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GTN-ED: كشف الحدث باستخدام شبكات محول الرسم البياني
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تظهر الأعمال الحديثة أن هيكل الرسم البياني للجملات، التي تم إنشاؤها من محلل التبعية، لديها إمكانات لتحسين اكتشاف الحدث.ومع ذلك، فإنهم غالبا ما يستفيدون فقط من الحواف (التبعيات) بين الكلمات، وتجاهل ملصقات التبعية (على سبيل المثال، الموضوع الاسمي)، معاملة حواف الرسم البياني الأساسي على أنها متجانسة.في هذا العمل، نقترح إطارا جديدا لإدماج كل من التبعيات والملصقات الخاصة بهم باستخدام تقنية اقترح مؤخرا تسمى شبكة محول الرسم البياني (GTN).نحن ندمج GTN للاستفادة من علاقات التبعية على نماذج مستقلة من الرسوم البيانية الحالية وتظهر تحسن في درجة F1 على مجموعة بيانات ACE.
Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection. However, they often only leverage the edges (dependencies) between words, and discard the dependency labels (e.g., nominal-subject), treating the underlying graph edges as homogeneous. In this work, we propose a novel framework for incorporating both dependencies and their labels using a recently proposed technique called Graph Transformer Network (GTN). We integrate GTN to leverage dependency relations on two existing homogeneous-graph-based models and demonstrate an improvement in the F1 score on the ACE dataset.
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The goal of Event Factuality Prediction (EFP) is to determine the factual degree of an event mention, representing how likely the event mention has happened in text. Current deep learning models has demonstrated the importance of syntactic and semant
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We propose the Recursive Non-autoregressive Graph-to-Graph Transformer architecture (RNGTr) for the iterative refinement of arbitrary graphs through the recursive application of a non-autoregressive Graph-to-Graph Transformer and apply it to syntacti
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