@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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}