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Register a new userYoungSheldon at SemEval-2021 Task 7: Fine-tuning Is All You Need
Youngsheldon في Semeval-2021 Task 7: Tuning Fine-Tuning هو كل ما تحتاجه
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في هذه الورقة، نصف نظامنا المستخدم في مهمة Semeval 2021 7: hahackathon: الكشف عن الفكاهة والجريمة.استخدمنا نهجا بسيطا للضبط باستخدام نماذج لغة مدربة مسبقا مختلفة (PLMS) لتقييم أدائها للكشف عن الفكاهة والجريمة.بالنسبة لمهام الانحدار، بلغنا متوسط عدد النماذج المختلفة التي تؤدي إلى أداء أفضل من النماذج الأصلية.شاركنا في جميع المساحات الفرعية.تم تصنيف نظامنا الأفضل أداء في المرتبة 4 في SubTask 1-B، 8 في SubTask 1-C، 12 في SubTask 2، وأداء جيدا في التركيز 1-A.نوضح نتائج شاملة أخرى باستخدام نماذج لغة مدربة مسبقا مسبقا والتي ستساعد على أنها خطوط أساس للعمل في المستقبل.
In this paper, we describe our system used for SemEval 2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense. We used a simple fine-tuning approach using different Pre-trained Language Models (PLMs) to evaluate their performance for humor and offense detection. For regression tasks, we averaged the scores of different models leading to better performance than the original models. We participated in all SubTasks. Our best performing system was ranked 4 in SubTask 1-b, 8 in SubTask 1-c, 12 in SubTask 2, and performed well in SubTask 1-a. We further show comprehensive results using different pre-trained language models which will help as baselines for future work.
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