@inproceedings{al-bashabsheh-abu-alasal-2021-es,
title = "{ES}-{JUST} at {S}em{E}val-2021 Task 7: Detecting and Rating Humor and Offensive Text Using Deep Learning",
author = "Al Bashabsheh, Emran and
Abu Alasal, Sanaa",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.153",
doi = "10.18653/v1/2021.semeval-1.153",
pages = "1102--1107",
abstract = "This research presents the work of the team{'}s ES-JUST at semEval-2021 task 7 for detecting and rating humor and offensive text using deep learning. The team evaluates several approaches (\textit{i.e.Bert, Roberta, XLM-Roberta, and Bert embedding + Bi-LSTM}) that employ in four sub-tasks. The first sub-task deal with whether the text is humorous or not. The second sub-task is the degree of humor in the text if the first sub-task is humorous. The third sub-task represents the text is controversial or not if it is humorous. While in the last task is the degree of an offensive in the text. However, Roberta pre-trained model outperforms other approaches and score the highest in all sub-tasks. We rank on the leader board at the evaluation phase are 14, 15, 20, and 5 through 0.9564 F-score, 0.5709 RMSE, 0.4888 F-score, and 0.4467 RMSE results, respectively, for each of the first, second, third, and fourth sub-task, respectively.",
}
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<abstract>This research presents the work of the team’s ES-JUST at semEval-2021 task 7 for detecting and rating humor and offensive text using deep learning. The team evaluates several approaches (i.e.Bert, Roberta, XLM-Roberta, and Bert embedding + Bi-LSTM) that employ in four sub-tasks. The first sub-task deal with whether the text is humorous or not. The second sub-task is the degree of humor in the text if the first sub-task is humorous. The third sub-task represents the text is controversial or not if it is humorous. While in the last task is the degree of an offensive in the text. However, Roberta pre-trained model outperforms other approaches and score the highest in all sub-tasks. We rank on the leader board at the evaluation phase are 14, 15, 20, and 5 through 0.9564 F-score, 0.5709 RMSE, 0.4888 F-score, and 0.4467 RMSE results, respectively, for each of the first, second, third, and fourth sub-task, respectively.</abstract>
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%0 Conference Proceedings
%T ES-JUST at SemEval-2021 Task 7: Detecting and Rating Humor and Offensive Text Using Deep Learning
%A Al Bashabsheh, Emran
%A Abu Alasal, Sanaa
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F al-bashabsheh-abu-alasal-2021-es
%X This research presents the work of the team’s ES-JUST at semEval-2021 task 7 for detecting and rating humor and offensive text using deep learning. The team evaluates several approaches (i.e.Bert, Roberta, XLM-Roberta, and Bert embedding + Bi-LSTM) that employ in four sub-tasks. The first sub-task deal with whether the text is humorous or not. The second sub-task is the degree of humor in the text if the first sub-task is humorous. The third sub-task represents the text is controversial or not if it is humorous. While in the last task is the degree of an offensive in the text. However, Roberta pre-trained model outperforms other approaches and score the highest in all sub-tasks. We rank on the leader board at the evaluation phase are 14, 15, 20, and 5 through 0.9564 F-score, 0.5709 RMSE, 0.4888 F-score, and 0.4467 RMSE results, respectively, for each of the first, second, third, and fourth sub-task, respectively.
%R 10.18653/v1/2021.semeval-1.153
%U https://aclanthology.org/2021.semeval-1.153
%U https://doi.org/10.18653/v1/2021.semeval-1.153
%P 1102-1107
Markdown (Informal)
[ES-JUST at SemEval-2021 Task 7: Detecting and Rating Humor and Offensive Text Using Deep Learning](https://aclanthology.org/2021.semeval-1.153) (Al Bashabsheh & Abu Alasal, SemEval 2021)
ACL