hhuEDOS at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) Binary Sexism Detection (Subtask A)

Wiebke Petersen, Diem-Ly Tran, Marion Wroblewitz

Abstract
In this paper, we describe SemEval-2023 Task 10, a shared task on detecting and predicting sexist language. The dataset consists of labeled sexist and non-sexist data targeted towards women acquired from both Reddit and Gab. We present and compare several approaches we experimented with and our final submitted model. Additional error analysis is given to recognize challenges we dealt with in our process. A total of 84 teams participated. Our model ranks 55th overall in Subtask A of the shared task.
Anthology ID:
2023.semeval-1.203
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:
1476–1482
Language:
URL:
https://aclanthology.org/2023.semeval-1.203
DOI:
10.18653/v1/2023.semeval-1.203
Bibkey:
Cite (ACL):
Wiebke Petersen, Diem-Ly Tran, and Marion Wroblewitz. 2023. hhuEDOS at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) Binary Sexism Detection (Subtask A). In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1476–1482, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
hhuEDOS at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) Binary Sexism Detection (Subtask A) (Petersen et al., SemEval 2023)
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