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Register a new userRoMa at SemEval-2021 Task 7: A Transformer-based Approach for Detecting and Rating Humor and Offense
روما في Semeval-2021 المهمة 7: نهج قائم على المحولات للكشف عن الفكاهة والجريمة
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في هذه الورقة، نصف النظم المستخدمة من قبل فريق الروما في المهمة المشتركة بشأن الكشف عن الفكاهة والفكاهة والجريمة (HAHAHACHATHON) في Semeval 2021. تعتمد أنظمتنا على تمثيلات البيانات المستفادة من خلال نماذج اللغة العصبية التي تم ضبطها بشكل جيد. على وجه الخصوص، نستكشف اثنين من البدينات المميزة. يعتمد أول واحد على شبكة عصبية سيام (SNN) مجتمعة مع طريقة التجميع المستندة إلى الرسم البياني. يستخدم نموذج SNN لتعلم مساحة كامنة حيث يمكن تمييز مثيلات الفكاهة وغير الفكاهة. يتم تطبيق طريقة التجميع لبناء النماذج الأولية لكلتا الفئتين المستخدمة في تدريب وتصنيف الرسائل الجديدة. يجمع المرء الثاني بين تمثيلات نموذج اللغة العصبية مع نموذج الانحدار الخطي الذي يجعل التصنيفات النهائية. حققت أنظمتنا أفضل نتائج لتصنيف فكاهة باستخدام نموذج واحد، في حين أن التصنيف الهجومي والفكاهة، حصل النموذج الثاني على أداء أفضل. في حالة التنبؤ الفكاهة المثيرة للجدل، تم تحقيق أهم تحسن من خلال ضبط طراز اللغة العصبية. بشكل عام، فإن النتائج المحققة مشجعة وتعطينا نقطة انطلاق لمزيد من التحسينات.
In this paper we describe the systems used by the RoMa team in the shared task on Detecting and Rating Humor and Offense (HaHackathon) at SemEval 2021. Our systems rely on data representations learned through fine-tuned neural language models. Particularly, we explore two distinct architectures. The first one is based on a Siamese Neural Network (SNN) combined with a graph-based clustering method. The SNN model is used for learning a latent space where instances of humor and non-humor can be distinguished. The clustering method is applied to build prototypes of both classes which are used for training and classifying new messages. The second one combines neural language model representations with a linear regression model which makes the final ratings. Our systems achieved the best results for humor classification using model one, whereas for offensive and humor rating the second model obtained better performance. In the case of the controversial humor prediction, the most significant improvement was achieved by a fine-tuning of the neural language model. In general, the results achieved are encouraging and give us a starting point for further improvements.
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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
or 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.
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.
With the emerging trends of using online platforms, peoples are increasingly interested in express their opinion through humorous texts. Identifying and rating humorous texts poses unique challenges to NLP due to subjective phenomena i.e. humor may v
ary to gender, profession, age, and classes of people. Besides, words with multiple senses, cultural domain, and pragmatic competence also need to be considered. A humorous text may be offensive to others. To address these challenges SemEval-2021 introduced a HaHackathon task focusing on detecting and rating humorous and offensive texts. This paper describes our participation in this task. We employed a stacked embedding and fine-tuned transformer models based classification and regression approach from the features from GPT2 medium, BERT, and RoBERTa transformer models. Besides, we utilized the fine-tuned BERT and RoBERTa models to examine the performances. Our method achieved competitive performances in this task.
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.
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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