توفر هذه الورقة نظرة عامة على المهمة المشتركة WANLP 2021 بشأن السخرية والكشف عن المعنويات باللغة العربية.المهمة المشتركة لها مفتاحان فرعي: الكشف عن السخرية (الفرعية 1) وتحليل المعرفات (SubTask 2).تهدف هذه المهمة المشتركة إلى الترويج والاهتمام بالكشف عن السخرية العربية، وهو أمر بالغ الأهمية لتحسين الأداء في مهام أخرى مثل تحليل المعرفات.تتكون DataSet المستخدمة في هذه المهمة المشتركة، وهي Arsarcasm-V2، من 15،548 تغريدات تسمى السخرية والشعور واللهجة.تلقينا 27 و 22 عروضا للمجموعات الفرعية 1 و 2 على التوالي.تعتمد معظم النهج على استخدام النماذج اللغوية المدربة مسبقا وضبطها جيدا مثل أرابيرت وماربرت.وكانت أفضل النتائج التي تحققت في مهام تحليل السخرية وتحليل المعنويات 0.6225 F1 و 0.748 F1-PN على التوالي.
This paper provides an overview of the WANLP 2021 shared task on sarcasm and sentiment detection in Arabic. The shared task has two subtasks: sarcasm detection (subtask 1) and sentiment analysis (subtask 2). This shared task aims to promote and bring attention to Arabic sarcasm detection, which is crucial to improve the performance in other tasks such as sentiment analysis. The dataset used in this shared task, namely ArSarcasm-v2, consists of 15,548 tweets labelled for sarcasm, sentiment and dialect. We received 27 and 22 submissions for subtasks 1 and 2 respectively. Most of the approaches relied on using and fine-tuning pre-trained language models such as AraBERT and MARBERT. The top achieved results for the sarcasm detection and sentiment analysis tasks were 0.6225 F1-score and 0.748 F1-PN respectively.
References used
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