Abstract
The “HaHackathon: Detecting and Rating Humor and Offense” task at the SemEval 2021 competition focuses on detecting and rating the humor level in sentences, as well as the level of offensiveness contained in these texts with humoristic tones. In this paper, we present an approach based on recent Deep Learning techniques by both trying to train the models based on the dataset solely and by trying to fine-tune pre-trained models on the gigantic corpus.- Anthology ID:
- 2021.semeval-1.174
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1226–1231
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.174
- DOI:
- 10.18653/v1/2021.semeval-1.174
- Cite (ACL):
- Mihai Samson and Daniela Gifu. 2021. FII FUNNY at SemEval-2021 Task 7: HaHackathon: Detecting and rating Humor and Offense. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1226–1231, Online. Association for Computational Linguistics.
- Cite (Informal):
- FII FUNNY at SemEval-2021 Task 7: HaHackathon: Detecting and rating Humor and Offense (Samson & Gifu, SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.174.pdf