تصف هذه الورقة نظامنا للمهمة 4 من Semeval-2021: قراءة الفهم من معنى مجردة (Recam).شاركنا في جميع المهام الفرعية حيث كان الهدف الرئيسي هو التنبؤ بكلمة مجردة مفقودة من بيان.نحن نضرب نماذج اللغة الملثمين المدربة مسبقا وهي بيرت وألبرت واستخدمت فرقة لهؤلاء كأنها نظامنا المقدمة على المراكز الفرعية 1 (إعادة التقييم - عقيدة) و Subtask 2 (إعادة التقييد - غير المعقدة).بالنسبة إلى Subtask 3 (تقاطع إعادة التقييد)، أرسلنا نموذج ألبرت لأنه يعطي أفضل النتائج.حاولنا نهج متعددة وجدنا أن النهج القائم على نمذجة اللغة الملثم (MLM) يعمل الأفضل.
This paper describes our system for Task 4 of SemEval-2021: Reading Comprehension of Abstract Meaning (ReCAM). We participated in all subtasks where the main goal was to predict an abstract word missing from a statement. We fine-tuned the pre-trained masked language models namely BERT and ALBERT and used an Ensemble of these as our submitted system on Subtask 1 (ReCAM-Imperceptibility) and Subtask 2 (ReCAM-Nonspecificity). For Subtask 3 (ReCAM-Intersection), we submitted the ALBERT model as it gives the best results. We tried multiple approaches and found that Masked Language Modeling(MLM) based approach works the best.
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