تصف هذه الورقة نظامنا للحصول على مهمة Semeval-2021 4: قراءة الفهم من معنى مجردة.لإنجاز هذه المهمة، نستخدم الهندسة المعمارية لشبكة إيلاءات الرسوم البيانية المعززة للمعرفة مع استراتيجية تحويل الفضاء الدلالي الردد.إنه يرفع المعرفة غير المتجانسة لتعلم الأدلة الكافية، ويسعى للحصول على مساحة دلالية فعالة من المفاهيم المجردة لتحسين قدرة الجهاز بشكل أفضل على فهم المعنى التجريدي للغة الطبيعية.تظهر النتائج التجريبية أن نظامنا يحقق أداء قويا في هذه المهمة من حيث كلا من غير المحتملة وغير المعقدة.
This paper describes our system for SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning. To accomplish this task, we utilize the Knowledge-Enhanced Graph Attention Network (KEGAT) architecture with a novel semantic space transformation strategy. It leverages heterogeneous knowledge to learn adequate evidences, and seeks for an effective semantic space of abstract concepts to better improve the ability of a machine in understanding the abstract meaning of natural language. Experimental results show that our system achieves strong performance on this task in terms of both imperceptibility and nonspecificity.
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