السخرية هي واحدة من التحديات الرئيسية لأنظمة تحليل المعنويات بسبب استخدام الصياغة غير المباشرة الضمنية للتعبير عن الآراء، وخاصة باللغة العربية.تقدم هذه الورقة النظام الذي قدمناه إلى المهمة الكشف عن السخرية والشاحنات الخاصة بمهمة WANLP-2021 القادرة على التعامل مع كل من المهارات الفرعية.نقوم أولا بإجراء ضبط جيد على نوعين من نماذج اللغة المدربة مسبقا (PLMS) مع استراتيجيات تدريب مختلفة.ثم يتم تطبيق آلية تكديس فعالة على رأس Plms المصنفات الدقيقة للحصول على التنبؤ النهائي.النتائج التجريبية على DataSet Arsarcasm-V2 تظهر فعالية طريقتنا ونحن نحتل المرتبة الثالثة والثانية للحصول على التراكب الفرعي 1 و 2.
Sarcasm is one of the main challenges for sentiment analysis systems due to using implicit indirect phrasing for expressing opinions, especially in Arabic. This paper presents the system we submitted to the Sarcasm and Sentiment Detection task of WANLP-2021 that is capable of dealing with both two subtasks. We first perform fine-tuning on two kinds of pre-trained language models (PLMs) with different training strategies. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on ArSarcasm-v2 dataset show the effectiveness of our method and we rank third and second for subtask 1 and 2.
References used
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