تصف هذه الورقة نظام الفوز بمهمة Semeval-2021 7: الكشف عن الفكاهة والجريمة.استراتيجيتنا هي تكديس نماذج لغوية مدربة مسبقا متنوعة (PLMS) مثل روبرتا وألبرت.نقوم أولا بإجراء ضبط جيد على هذين اللقطتين مع مختلف أنواع مختلفة واستراتيجيات التدريب المختلفة.ثم يتم تطبيق آلية تكديس صالحة على رأس Plms المصنفات بشكل جيد للحصول على التنبؤ النهائي.تظهر النتائج التجريبية على DataSet الصادر عن منظم المهمة صحة طريقتنا ونفز في المركز الأول والمركز الثالث ل SubTask 2 و 1A.
This paper describes the winning system for SemEval-2021 Task 7: Detecting and Rating Humor and Offense. Our strategy is stacking diverse pre-trained language models (PLMs) such as RoBERTa and ALBERT. We first perform fine-tuning on these two PLMs with various hyperparameters and different training strategies. Then a valid stacking mechanism is applied on top of the fine-tuned PLMs to get the final prediction. Experimental results on the dataset released by the organizer of the task show the validity of our method and we win first place and third place for subtask 2 and 1a.
References used
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Humor recognition is a challenging task in natural language processing. This document presents my approaches to detect and rate humor and offense from the given text. This task includes 2 tasks: task 1 which contains 3 subtasks (1a, 1b, and 1c), and
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