Abstract
The SemEval 2023 shared task 10 “Explainable Detection of Online Sexism” focuses on detecting and identifying comments and tweets containing sexist expressions and also explaining why it is sexist.This paper describes our system that we used to participate in this shared task. Our model is an ensemble of different variants of fine tuned DeBERTa models that employs a k-fold cross-validation. We have participated in the three tasks A, B and C. Our model ranked 2 nd position in tasks A, 7 th in task B and 4 th in task C.- Anthology ID:
- 2023.semeval-1.252
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1824–1832
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.252
- DOI:
- 10.18653/v1/2023.semeval-1.252
- Cite (ACL):
- Fadi Hassan, Abdessalam Bouchekif, and Walid Aransa. 2023. FiRC at SemEval-2023 Task 10: Fine-grained Classification of Online Sexism Content Using DeBERTa. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1824–1832, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- FiRC at SemEval-2023 Task 10: Fine-grained Classification of Online Sexism Content Using DeBERTa (Hassan et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2023.semeval-1.252.pdf