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Semeval 2021 المهمة 7: Hahackathon والكشف عن الفكاهة والجريمة
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Semeval 2021 المهمة 7، Hahackathon، كانت أول مهمة مشتركة للجمع بين المجالات المنفصلة سابقا من الكشف عن الفكاهة والكشف عن الجريمة. جمعنا 10000 نص من تويتر ومجموعات بيانات النكات القصيرة في Kaggle، وكان كل منها مشروح من الفكاهة والجريمة بمقدار 20 حديثا في سن 18-70. كانتنا فرعيتنا الفرعية للكشف عن الفكاهة الثنائية، والتنبؤ بتصنيفات الفكاهة والجريمة، ومهمة جدل جديدة: للتنبؤ إذا كان التباين في تصنيفات الفكاهة أعلى من عتبة محددة. جذبت المهن الفرعية 36-58 طلبا، مع اختيار معظم المشاركين استخدام نماذج اللغة المدربة مسبقا. كما نفذت العديد من الفرق الأعلى الأداء تقنيات تحسين إضافية، بما في ذلك التدريب على المهام على التكيف والتدريب الخصم. تشير النتائج إلى أن النظم المشاركة مناسبة تماما للكشف عن الفكاهة، ولكن هذه الخلافات الفكاهة مهمة أكثر تحديا. نناقش النماذج التي تتفوق في هذه المهمة، والتي تعزز التقنيات الإضافية أدائها، وتحليل الأخطاء التي لم يتم التقاطها من قبل أفضل الأنظمة.
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 humor and offense by 20 annotators aged 18-70. Our subtasks were binary humor detection, prediction of humor and offense ratings, and a novel controversy task: to predict if the variance in the humor ratings was higher than a specific threshold. The subtasks attracted 36-58 submissions, with most of the participants choosing to use pre-trained language models. Many of the highest performing teams also implemented additional optimization techniques, including task-adaptive training and adversarial training. The results suggest that the participating systems are well suited to humor detection, but that humor controversy is a more challenging task. We discuss which models excel in this task, which auxiliary techniques boost their performance, and analyze the errors which were not captured by the best systems.
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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
paper, we present an approach based on recent Deep Learning techniques by both trying to train the models based on the dataset solely and by trying to fine-tune pre-trained models on the gigantic corpus.
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,
BERTweet, RoBERTa base and Large, and RoBERTa base irony, for detecting and rating humor and offense. The proposed models, where given a text in cased and uncased type obtained from SemEval-2021 Task7: HaHackathon: Linking Humor and Offense Across Different Age Groups. The highest scored model for the first subtask: Humor Detection, is BERTweet base cased model with 0.9540 F1-score, for the second subtask: Average Humor Rating Score, it is BERT Large cased with the minimum RMSE of 0.5555, for the fourth subtask: Average Offensiveness Rating Score, it is BERTweet base cased model with minimum RMSE of 0.4822.
This article introduces the submission of subtask 1 and subtask 2 that we participate in SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense, we use a model based on ALBERT that uses ALBERT as the module for extracting text featu
res. We modify the upper layer structure by adding specific networks to better summarize the semantic information. Finally, our system achieves an F-Score of 0.9348 in subtask 1a, RMSE of 0.7214 in subtask 1b, F-Score of 0.4603 in subtask 1c, and RMSE of 0.5204 in subtask 2.
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
.Sub-task 1a is to predict if the text would beconsidered humorous.Sub-task 1c is described asfollows: if the text is classed as humorous, predictif the humor rating would be considered controver-sial, i.e. the variance of the rating between annota-tors is higher than the median.we combined threepre-trained model with CNN to complete these twoclassification sub-tasks.Sub-task 1b is to judge thedegree of humor.Sub-task 2 aims to predict how of-fensive a text would be with values between 0 and5.We use the idea of regression to deal with thesetwo sub-tasks.We analyze the performance of ourmethod and demonstrate the contribution of eachcomponent of our architecture.We have achievedgood results under the combination of multiple pre-training models and optimization methods.
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.
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