@inproceedings{rydelek-etal-2023-adamr,
title = "{A}dam{R} at {S}em{E}val-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning",
author = "Rydelek, Adam and
Dementieva, Daryna and
Groh, Georg",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.190",
doi = "10.18653/v1/2023.semeval-1.190",
pages = "1371--1381",
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.",
}
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<title>Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)</title>
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<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.</abstract>
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%0 Conference Proceedings
%T AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning
%A Rydelek, Adam
%A Dementieva, Daryna
%A Groh, Georg
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F rydelek-etal-2023-adamr
%X 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.
%R 10.18653/v1/2023.semeval-1.190
%U https://aclanthology.org/2023.semeval-1.190
%U https://doi.org/10.18653/v1/2023.semeval-1.190
%P 1371-1381
Markdown (Informal)
[AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning](https://aclanthology.org/2023.semeval-1.190) (Rydelek et al., SemEval 2023)
ACL