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Register a new userBERT-based Multi-Task Model for Country and Province Level MSA and Dialectal Arabic Identification
نموذج متعدد المهام مقرها بيرت لمقاطعة MSA والحمولية الهوية العربية
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الهوية واللغة القياسية الهوية هي مهام حاسمة للعديد من تطبيقات معالجة اللغة العربية.في هذه الورقة، نقدم نظامنا القائم على التعلم العميق، المقدم إلى المهمة المشتركة الثانية من النادي الثاني لتحديد المستوى القطري على مستوى المحافظة على اللغة العربية المعيارية الحديثة (MSA) واللهولية العربية (DA).يعتمد النظام على نموذج تعليمي عميق متعدد الإنهائي (MTL) لمعالجة كلا من الرتبة البلد والمستوى من المستوى MSA / DA.يتكون نموذج MTL الأخير من محولات تمثيل ترميز ترميز مشترك (بيرت)، طبقات اهتمام خاصتين بمهام العمل، واثنين من المصنفين.تتمثل فكرتنا الرئيسية في الاستفادة من كل من التمييز على المهمة والميزات المشتركة بين المهام للبلد والمقاطعة MSA / DA الهوية.تظهر النتائج التي تم الحصول عليها أن نموذج MTL يتفوق على نماذج مهمة واحدة في معظم المهام الفرعية.
Dialect and standard language identification are crucial tasks for many Arabic natural language processing applications. In this paper, we present our deep learning-based system, submitted to the second NADI shared task for country-level and province-level identification of Modern Standard Arabic (MSA) and Dialectal Arabic (DA). The system is based on an end-to-end deep Multi-Task Learning (MTL) model to tackle both country-level and province-level MSA/DA identification. The latter MTL model consists of a shared Bidirectional Encoder Representation Transformers (BERT) encoder, two task-specific attention layers, and two classifiers. Our key idea is to leverage both the task-discriminative and the inter-task shared features for country and province MSA/DA identification. The obtained results show that our MTL model outperforms single-task models on most subtasks.
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