AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 15 May 2023.
Builds on
11 of 29 references.
References · 11
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- 2023SemEval-2023 Task 10: Explainable Detection of Online Sexismcited by 48
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Citing papers per year: 2023: 1.
1 paper.