AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning
This work addresses the challenge of detecting and explaining online sexism, which is an incremental improvement for researchers and practitioners in natural language processing and social media analysis.
The paper tackled the class imbalance problem in sexism detection by using ensemble learning with Transformer models, achieving a ranking in the top 40% of teams across all tracks.
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.