Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userhub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense
HUB في Semeval-2021 المهمة 7: الانصهار في ألبرت ومعلومات تردد الكلمات الكشف عن الفكاهة والجريمة
501
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تقدم هذه الورقة وصف نظام فريق المحور، الذي يفسر العمل ذي الصلة والنتائج التجريبية لمشاركة فريقنا في مهمة Semeval 2021 7: Hahackathon: الكشف عن الفكاهة والجريمة. لقد نجحنا بنجاح في نتائج التنبؤ بنتائج الاختبار للمجموعة الفرعية في المهمة. الهدف من المهمة هو إجراء الكشف عن الفكاهة، وتقييم الصف، والتقييم الهجومي على كل بيانات نصية إنجليزية في مجموعة البيانات. يمكن تقسيم المهام إلى نوعين من المهن الفرعية. واحد هو مهمة تصنيف النص، والآخر هو مهمة الانحدار النصي. ما نحتاج إليه هو استخدام طريقتنا للكشف عن المعلومات الفكاهة والهجومية من الجملة بدقة قدر الإمكان. تتكون الأساليب المستخدمة في النتائج المقدمة من فريقنا أساسا من خوارزميات ألبرت وشبك سي إن إن و TF-جيش الدفاع الإسرائيلي. إن مؤشرات تقييم النتائج المقدمة من مهمة التصنيف هي درجة ودقة F1. مؤشر تقييم النتائج لتقديم مهمة الانحدار هو RMSE. النتائج النهائية لنتائج التنبؤ لمجموعات الاختبار الفرعي المقدم من فريقنا هي Task1a 0.921 (F1)، TASK1A 0.9364 (الدقة)، TASK1B 0.6288 (RMSE)، TASK1C 0.5333 (F1)، TASK1C 0.0.5591 (الدقة)، و TASK2 0.5027 (RMSE) على التوالي.
This paper introduces the system description of the hub team, which explains the related work and experimental results of our team's participation in SemEval 2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense. We successfully submitted the test set prediction results of the two subtasks in the task. The goal of the task is to perform humor detection, grade evaluation, and offensive evaluation on each English text data in the data set. Tasks can be divided into two types of subtasks. One is a text classification task, and the other is a text regression task. What we need to do is to use our method to detect the humor and offensive information of the sentence as accurately as possible. The methods used in the results submitted by our team are mainly composed of ALBERT, CNN, and Tf-Idf algorithms. The result evaluation indicators submitted by the classification task are F1 score and Accuracy. The result evaluation index of the regression task submission is the RMSE. The final scores of the prediction results of the two subtask test sets submitted by our team are task1a 0.921 (F1), task1a 0.9364 (Accuracy), task1b 0.6288 (RMSE), task1c 0.5333 (F1), task1c 0.0.5591 (Accuracy), and task2 0.5027 (RMSE) respectively.
References used
https://aclanthology.org/
rate research
Read More
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.
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 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.
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.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
