@inproceedings{tasneem-etal-2023-kingsmantrio,
title = "{K}ingsman{T}rio at {S}em{E}val-2023 Task 10: Analyzing the Effectiveness of Transfer Learning Models for Explainable Online Sexism Detection",
author = "Tasneem, Fareen and
Hossain, Tashin and
Naim, Jannatun",
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-1.263",
doi = "10.18653/v1/2023.semeval-1.263",
pages = "1916--1920",
abstract = "Online social platforms are now propagating sexist content endangering the involvement and inclusion of women on these platforms. Sexism refers to hostility, bigotry, or discrimination based on gender, typically against women. The proliferation of such notions deters women from engaging in social media spontaneously. Hence, detecting sexist content is critical to ensure a safe online platform where women can participate without the fear of being a target of sexism. This paper describes our participation in subtask A of SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This subtask requires classifying textual content as sexist or not sexist. We incorporate a RoBERTa-based architecture and further finetune the hyperparameters to entail better performance. The procured results depict the competitive performance of our approach among the other participants.",
}
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<abstract>Online social platforms are now propagating sexist content endangering the involvement and inclusion of women on these platforms. Sexism refers to hostility, bigotry, or discrimination based on gender, typically against women. The proliferation of such notions deters women from engaging in social media spontaneously. Hence, detecting sexist content is critical to ensure a safe online platform where women can participate without the fear of being a target of sexism. This paper describes our participation in subtask A of SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This subtask requires classifying textual content as sexist or not sexist. We incorporate a RoBERTa-based architecture and further finetune the hyperparameters to entail better performance. The procured results depict the competitive performance of our approach among the other participants.</abstract>
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%0 Conference Proceedings
%T KingsmanTrio at SemEval-2023 Task 10: Analyzing the Effectiveness of Transfer Learning Models for Explainable Online Sexism Detection
%A Tasneem, Fareen
%A Hossain, Tashin
%A Naim, Jannatun
%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 tasneem-etal-2023-kingsmantrio
%X Online social platforms are now propagating sexist content endangering the involvement and inclusion of women on these platforms. Sexism refers to hostility, bigotry, or discrimination based on gender, typically against women. The proliferation of such notions deters women from engaging in social media spontaneously. Hence, detecting sexist content is critical to ensure a safe online platform where women can participate without the fear of being a target of sexism. This paper describes our participation in subtask A of SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This subtask requires classifying textual content as sexist or not sexist. We incorporate a RoBERTa-based architecture and further finetune the hyperparameters to entail better performance. The procured results depict the competitive performance of our approach among the other participants.
%R 10.18653/v1/2023.semeval-1.263
%U https://aclanthology.org/2023.semeval-1.263
%U https://doi.org/10.18653/v1/2023.semeval-1.263
%P 1916-1920
Markdown (Informal)
[KingsmanTrio at SemEval-2023 Task 10: Analyzing the Effectiveness of Transfer Learning Models for Explainable Online Sexism Detection](https://aclanthology.org/2023.semeval-1.263) (Tasneem et al., SemEval 2023)
ACL