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Register a new userCountry-level Arabic Dialect Identification using RNNs with and without Linguistic Features
الهوية العربية على المستوى القطري باستخدام RNNS مع وبدون ميزات لغوية
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يحقق هذا العمل في قيمة زيادة الشبكات العصبية المتكررة مع هندسة ميزة لهندسة الطلب العربي الثاني (NADI) 1.2: تحديد المستوى القطري.نقارن أداء LSTM على مستوى الكلمات البسيطة باستخدام Artrained Abbeddings مع واحدة معززة باستخدام ميزة الميزات للميزات اللغوية المهندسة.تظهر نتائجنا أن إضافة ميزات صريحة إلى LSTM ضارية للأداء.نحن نعزو هذا فقدان الأداء على التبريد في بعض العناصر اللغوية في بعض النصوص، وكلية المواضيع، والتنقل المشارك.
This work investigates the value of augmenting recurrent neural networks with feature engineering for the Second Nuanced Arabic Dialect Identification (NADI) Subtask 1.2: Country-level DA identification. We compare the performance of a simple word-level LSTM using pretrained embeddings with one enhanced using feature embeddings for engineered linguistic features. Our results show that the addition of explicit features to the LSTM is detrimental to performance. We attribute this performance loss to the bivalency of some linguistic items in some text, ubiquity of topics, and participant mobility.
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