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Register a new userDialect Identification in Nuanced Arabic Tweets Using Farasa Segmentation and AraBERT
تحديد الهياكل في تغريدات عربية محددة باستخدام تجزئة Farasa
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تقدم هذه الورقة نهجنا لمعالجة المهمة المشتركة EACL WANLP-2021 1: تحديد الهلام العربي الدقيق (NADI).تهدف المهمة إلى تطوير نظام يحدد الموقع الجغرافي (البلد / المقاطعة) من مكان وجود تغريدة عربية في شكل لغة عربية أو لهجة قياسية حديثة تأتي من.نحن نحل المهمة في جزأين.ينطوي الجزء الأول على معالجة البيانات المقدمة مسبقا عن طريق التنظيف وإضافة وأجزاء مختلفة من النص.يتبع ذلك إجراء تجارب مع إصدارات مختلفة من النماذج القائمة على المحولات، أرابيرت وأعريليكترا.حقق نهجنا النهائي درجات ماكرو F1 من 0.216، 0.235، 0.054، و 0.043 في الترقيم الفرعي الأربع، وتم تصنيفنا في المرتبة الثانية في المهام الفرعية لتعريف MSA والرابع في عمليات تحديد الهوية الفرعية.
This paper presents our approach to address the EACL WANLP-2021 Shared Task 1: Nuanced Arabic Dialect Identification (NADI). The task is aimed at developing a system that identifies the geographical location(country/province) from where an Arabic tweet in the form of modern standard Arabic or dialect comes from. We solve the task in two parts. The first part involves pre-processing the provided dataset by cleaning, adding and segmenting various parts of the text. This is followed by carrying out experiments with different versions of two Transformer based models, AraBERT and AraELECTRA. Our final approach achieved macro F1-scores of 0.216, 0.235, 0.054, and 0.043 in the four subtasks, and we were ranked second in MSA identification subtasks and fourth in DA identification subtasks.
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