@inproceedings{rallabandi-etal-2023-sss,
title = "{SSS} at {S}em{E}val-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers",
author = "Rallabandi, Sriya and
Singhal, Sanchit and
Seth, Pratinav",
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-1.171",
doi = "10.18653/v1/2023.semeval-1.171",
pages = "1231--1236",
abstract = "This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise in social media platforms has seen an increase in disproportionate levels of sexism experienced by women on social media platforms. This has made detecting and explaining online sexist content more important than ever to make social media safer and more accessible for women. Our approach consists of experimenting and finetuning BERT-based models and using a Majority Voting ensemble model that outperforms individual baseline model scores. Our system achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319 for Task C.",
}
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<abstract>This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise in social media platforms has seen an increase in disproportionate levels of sexism experienced by women on social media platforms. This has made detecting and explaining online sexist content more important than ever to make social media safer and more accessible for women. Our approach consists of experimenting and finetuning BERT-based models and using a Majority Voting ensemble model that outperforms individual baseline model scores. Our system achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319 for Task C.</abstract>
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%0 Conference Proceedings
%T SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers
%A Rallabandi, Sriya
%A Singhal, Sanchit
%A Seth, Pratinav
%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 rallabandi-etal-2023-sss
%X This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise in social media platforms has seen an increase in disproportionate levels of sexism experienced by women on social media platforms. This has made detecting and explaining online sexist content more important than ever to make social media safer and more accessible for women. Our approach consists of experimenting and finetuning BERT-based models and using a Majority Voting ensemble model that outperforms individual baseline model scores. Our system achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319 for Task C.
%R 10.18653/v1/2023.semeval-1.171
%U https://aclanthology.org/2023.semeval-1.171
%U https://doi.org/10.18653/v1/2023.semeval-1.171
%P 1231-1236
Markdown (Informal)
[SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers](https://aclanthology.org/2023.semeval-1.171) (Rallabandi et al., SemEval 2023)
ACL