@inproceedings{pu-zhou-2023-pcj,
title = "{PCJ} at {S}em{E}val-2023 Task 10: A Ensemble Model Based on Pre-trained Model for Sexism Detection and Classification in {E}nglish",
author = "Pu, Chujun and
Zhou, Xiaobing",
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-1.59",
doi = "10.18653/v1/2023.semeval-1.59",
pages = "433--438",
abstract = "This paper describes the system and the resulting model submitted by our team {``}PCJ{''} to the SemEval-2023 Task 10 sub-task A contest. In this task, we need to test the English text content in the posts to determine whether there is sexism, which involves emotional text classification. Our submission system utilizes methods based on RoBERTa, SimCSE-RoBERTa pre-training models, and model ensemble to classify and train on datasets provided by the organizers. In the final assessment, our submission achieved a macro average F1 score of 0.8449, ranking 28th out of 84 teams in Task A.",
}
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<title>PCJ at SemEval-2023 Task 10: A Ensemble Model Based on Pre-trained Model for Sexism Detection and Classification in English</title>
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<abstract>This paper describes the system and the resulting model submitted by our team “PCJ” to the SemEval-2023 Task 10 sub-task A contest. In this task, we need to test the English text content in the posts to determine whether there is sexism, which involves emotional text classification. Our submission system utilizes methods based on RoBERTa, SimCSE-RoBERTa pre-training models, and model ensemble to classify and train on datasets provided by the organizers. In the final assessment, our submission achieved a macro average F1 score of 0.8449, ranking 28th out of 84 teams in Task A.</abstract>
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%0 Conference Proceedings
%T PCJ at SemEval-2023 Task 10: A Ensemble Model Based on Pre-trained Model for Sexism Detection and Classification in English
%A Pu, Chujun
%A Zhou, Xiaobing
%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 pu-zhou-2023-pcj
%X This paper describes the system and the resulting model submitted by our team “PCJ” to the SemEval-2023 Task 10 sub-task A contest. In this task, we need to test the English text content in the posts to determine whether there is sexism, which involves emotional text classification. Our submission system utilizes methods based on RoBERTa, SimCSE-RoBERTa pre-training models, and model ensemble to classify and train on datasets provided by the organizers. In the final assessment, our submission achieved a macro average F1 score of 0.8449, ranking 28th out of 84 teams in Task A.
%R 10.18653/v1/2023.semeval-1.59
%U https://aclanthology.org/2023.semeval-1.59
%U https://doi.org/10.18653/v1/2023.semeval-1.59
%P 433-438
Markdown (Informal)
[PCJ at SemEval-2023 Task 10: A Ensemble Model Based on Pre-trained Model for Sexism Detection and Classification in English](https://aclanthology.org/2023.semeval-1.59) (Pu & Zhou, SemEval 2023)
ACL