@inproceedings{rifat-etal-2023-acsmkrhr,
title = "{ACSMKRHR} at {S}em{E}val-2023 Task 10: Explainable Online Sexism Detection({EDOS})",
author = "Rifat, Rakib Hossain and
Shruti, Abanti and
Kamal, Marufa and
Sadeque, Farig",
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-1.99",
doi = "10.18653/v1/2023.semeval-1.99",
pages = "724--732",
abstract = "People are expressing their opinions online for a lot of years now. Although these opinions and comments provide people an opportunity of expressing their views, there is a lot of hate speech that can be found online. More specifically, sexist comments are very popular affecting and creating a negative impact on a lot of women and girls online. This paper describes the approaches of the SemEval-2023 Task 10 competition for Explainable Online Sexism Detection (EDOS). The task has been divided into 3 subtasks, introducing different classes of sexist comments. We have approached these tasks using the bert-cased and uncased models which are trained on the annotated dataset that has been provided in the competition. Task A provided the best F1 score of 80{\%} on the test set, and tasks B and C provided 58{\%} and 40{\%} respectively.",
}
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%0 Conference Proceedings
%T ACSMKRHR at SemEval-2023 Task 10: Explainable Online Sexism Detection(EDOS)
%A Rifat, Rakib Hossain
%A Shruti, Abanti
%A Kamal, Marufa
%A Sadeque, Farig
%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 rifat-etal-2023-acsmkrhr
%X People are expressing their opinions online for a lot of years now. Although these opinions and comments provide people an opportunity of expressing their views, there is a lot of hate speech that can be found online. More specifically, sexist comments are very popular affecting and creating a negative impact on a lot of women and girls online. This paper describes the approaches of the SemEval-2023 Task 10 competition for Explainable Online Sexism Detection (EDOS). The task has been divided into 3 subtasks, introducing different classes of sexist comments. We have approached these tasks using the bert-cased and uncased models which are trained on the annotated dataset that has been provided in the competition. Task A provided the best F1 score of 80% on the test set, and tasks B and C provided 58% and 40% respectively.
%R 10.18653/v1/2023.semeval-1.99
%U https://aclanthology.org/2023.semeval-1.99
%U https://doi.org/10.18653/v1/2023.semeval-1.99
%P 724-732
Markdown (Informal)
[ACSMKRHR at SemEval-2023 Task 10: Explainable Online Sexism Detection(EDOS)](https://aclanthology.org/2023.semeval-1.99) (Rifat et al., SemEval 2023)
ACL