Abstract
The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks. Our team experimented with different ways to combat class imbalance throughout the tasks using data augmentation and loss alteration techniques. We tackled the challenge by utilising ensembles of Transformer models trained on different datasets, which are tested to find the balance between performance and interpretability. This solution ranked us in the top 40% of teams for each of the tracks.- Anthology ID:
- 2023.semeval-1.190
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1371–1381
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.190
- DOI:
- 10.18653/v1/2023.semeval-1.190
- Cite (ACL):
- Adam Rydelek, Daryna Dementieva, and Georg Groh. 2023. AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1371–1381, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning (Rydelek et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.semeval-1.190.pdf