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.- Anthology ID:
- 2021.semeval-1.155
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1114–1119
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.155
- DOI:
- 10.18653/v1/2021.semeval-1.155
- Cite (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.
- Cite (Informal):
- DLJUST at SemEval-2021 Task 7: Hahackathon: Linking Humor and Offense (Al-Omari et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2021.semeval-1.155.pdf