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Register a new userUMUTeam at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Linguistic Features and Word Embeddings
UMUTEAM في مهمة Semeval-2021: الكشف عن الفكاهة والجريمة تصنيفها وتصنيفها مع الميزات اللغوية و Adgeddings Word
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في الكتابة، تعتمد الفكاهة بشكل رئيسي على اللغة المجازية التي تغير الكلمات والتعبيرات المعنى التقليدي للإشارة إلى شيء ما دون قوله مباشرة.يمنع هذا الوجه بمعنى الكلمات معالجة اللغات الطبيعية من الكشف عن النية الحقيقية للاتصال، وبالتالي، يقلل من فعالية المهام مثل تحليل المعنويات أو الكشف عن المشاعر.في هذه المخطوطة، نصف أن نصف مشاركة UMUTEAM في HAHACHATHON 2021، والتي يكون هدفها هو اكتشاف ومعدل محتوى مضحك ومثير للجدل.يستند اقتراحنا إلى مزيج من الميزات اللغوية مع تضمين الكلمات السياقية وغير السياقية.نشارك في جميع المساحات الفرعية المقترحة التي تحققت نتيجة أفضل النتائج في الفكاهة المثيرة للجدل.
In writing, humor is mainly based on figurative language in which words and expressions change their conventional meaning to refer to something without saying it directly. This flip in the meaning of the words prevents Natural Language Processing from revealing the real intention of a communication and, therefore, reduces the effectiveness of tasks such as Sentiment Analysis or Emotion Detection. In this manuscript we describe the participation of the UMUTeam in HaHackathon 2021, whose objective is to detect and rate humorous and controversial content. Our proposal is based on the combination of linguistic features with contextual and non-contextual word embeddings. We participate in all the proposed subtasks achieving our best result in the controversial humor subtask.
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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
.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.
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
th 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.
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.
This paper describes Humor-BERT, a set of BERT Large based models that we used in the SemEval-2021 Task 7: Detecting and Rating Humor and Offense. It presents pre and post processing techniques, variable threshold learning, meta learning and Ensemble
approach to solve various sub-tasks that were part of the challenge. We also present a comparative analysis of various models we tried. Our method was ranked 4th in Humor Controversy Detection, 8th in Humor Detection, 19th in Average Offense Score prediction and 40th in Average Humor Score prediction globally. F1 score obtained for Humor classification was 0.9655 and for Controversy detection it was 0.6261. Our user name on the leader board is ThisIstheEnd and team name is EndTimes.
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.
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