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    <abstract>Humor detection and rating poses interesting linguistic challenges to NLP; it is highly subjective depending on the perceptions of a joke and the context in which it is used. This paper utilizes and compares transformers models; BERT base and Large, BERTweet, RoBERTa base and Large, and RoBERTa base irony, for detecting and rating humor and offense. The proposed models, where given a text in cased and uncased type obtained from SemEval-2021 Task7: HaHackathon: Linking Humor and Offense Across Different Age Groups. The highest scored model for the first subtask: Humor Detection, is BERTweet base cased model with 0.9540 F1-score, for the second subtask: Average Humor Rating Score, it is BERT Large cased with the minimum RMSE of 0.5555, for the fourth subtask: Average Offensiveness Rating Score, it is BERTweet base cased model with minimum RMSE of 0.4822.</abstract>
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%0 Conference Proceedings
%T DLJUST at SemEval-2021 Task 7: Hahackathon: Linking Humor and Offense
%A Al-Omari, Hani
%A AbedulNabi, Isra’a
%A Duwairi, Rehab
%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-omari-etal-2021-dljust
%X Humor detection and rating poses interesting linguistic challenges to NLP; it is highly subjective depending on the perceptions of a joke and the context in which it is used. This paper utilizes and compares transformers models; BERT base and Large, BERTweet, RoBERTa base and Large, and RoBERTa base irony, for detecting and rating humor and offense. The proposed models, where given a text in cased and uncased type obtained from SemEval-2021 Task7: HaHackathon: Linking Humor and Offense Across Different Age Groups. The highest scored model for the first subtask: Humor Detection, is BERTweet base cased model with 0.9540 F1-score, for the second subtask: Average Humor Rating Score, it is BERT Large cased with the minimum RMSE of 0.5555, for the fourth subtask: Average Offensiveness Rating Score, it is BERTweet base cased model with minimum RMSE of 0.4822.
%R 10.18653/v1/2021.semeval-1.155
%U https://aclanthology.org/2021.semeval-1.155
%U https://doi.org/10.18653/v1/2021.semeval-1.155
%P 1114-1119

Markdown (Informal)

[DLJUST at SemEval-2021 Task 7: Hahackathon: Linking Humor and Offense](https://aclanthology.org/2021.semeval-1.155) (Al-Omari et al., SemEval 2021)

• DLJUST at SemEval-2021 Task 7: Hahackathon: Linking Humor and Offense (Al-Omari et al., SemEval 2021)

ACL

• Hani Al-Omari, Isra’a AbedulNabi, and Rehab Duwairi. 2021. DLJUST at SemEval-2021 Task 7: Hahackathon: Linking Humor and Offense. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1114–1119, Online. Association for Computational Linguistics.