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.

This paper presents the work of the NLP@JUST team at SemEval-2020 Task 4 competition that related to commonsense validation and explanation (ComVE) task. The team participates in sub-taskA (Validation) which related to validation that checks if the text is against common sense or not. Several models have trained (i.e. Bert, XLNet, and Roberta), however, the main models used are the RoBERTa-large and BERT Whole word masking. As well as, we utilized the results from both models to generate final prediction by using the average Ensemble technique, that used to improve the overall performance. The evaluation result shows that the implemented model achieved an accuracy of 93.9% obtained and published at the post-evaluation result on the leaderboard.