التعرف الفكاهي هو مهمة صعبة في معالجة اللغة الطبيعية.تقدم هذه الوثيقة مناهجاتي للكشف عن الفكاهة والجريمة من النص المحدد.تتضمن هذه المهمة مهام 2: المهمة 1 التي تحتوي على 3 مجموعات فرعية (1A، 1B، و 1C)، والمهمة 2. يمكن اعتبار 1A SubTask 1A و 1C مشاكل التصنيف واتخاذ ألبرت كنموذج أساسي.SubTask 1B و 2 يمكن أن ينظر إليها على أنها قضايا الانحدار وتأخذ روبرتا كنموذج أساسي.
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 task 2. Subtask 1a and 1c can be regarded as classification problems and take ALBERT as the basic model. Subtask 1b and 2 can be viewed as regression issues and take RoBERTa as the basic model.
References used
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This paper describes our contribution to SemEval-2021 Task 7: Detecting and Rating Humor and Of-fense.This task contains two sub-tasks, sub-task 1and sub-task 2. Among them, sub-task 1 containsthree sub-tasks, sub-task 1a ,sub-task 1b and sub-task 1c
SemEval 2021 Task 7, HaHackathon, was the first shared task to combine the previously separate domains of humor detection and offense detection. We collected 10,000 texts from Twitter and the Kaggle Short Jokes dataset, and had each annotated for hum
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 wi
Humor detection and rating poses interesting linguistic challenges to NLP; it is highly subjective depending on the perceptions of a joke and the context in which it is used. This paper utilizes and compares transformers models; BERT base and Large,
The HaHackathon: Detecting and Rating Humor and Offense'' task at the SemEval 2021 competition focuses on detecting and rating the humor level in sentences, as well as the level of offensiveness contained in these texts with humoristic tones. In this