ES-JUST at SemEval-2021 Task 7: Detecting and Rating Humor and Offensive Text Using Deep Learning

Emran Al Bashabsheh, Sanaa Abu Alasal


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.
Anthology ID:
2021.semeval-1.153
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1102–1107
Language:
URL:
https://aclanthology.org/2021.semeval-1.153
DOI:
10.18653/v1/2021.semeval-1.153
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.153.pdf