المساهمة الرئيسية لهذه الورقة هي نماذج اللغات القائمة على Tune-Tune - مدربة مسبقا على العديد من النصوص، وبعضها عام (على سبيل المثال، ويكيبيديا، bookscorpus)، وبعضها يجري شركة DataSet المعقدة، والبعض الآخر يجريمن مجالات محددة أخرى مثل التمويل والقانون، إلخ. نقوم بإجراء دراسات الاجتثاث حول اختيار طرازات المحولات وكيف يتم تجميع درجات تعقيدها الفردية للحصول على درجات التعقيد الناتجة.لدينا طريقة تحقق أفضل ارتباط بيرنسي ب 0.784 في المهمة الفرعية 1 (كلمة واحدة) و 0.836 في المهمة الفرعية 2 (تعبيرات كلمات متعددة).
The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).
References used
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