@inproceedings{yao-etal-2023-hitszq,
title = "{HITSZQ} at {S}em{E}val-2023 Task 10: Category-aware Sexism Detection Model with Self-training Strategy",
author = "Yao, Ziyi and
Chai, Heyan and
Cui, Jinhao and
Tang, Siyu and
Liao, Qing",
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-1.129",
doi = "10.18653/v1/2023.semeval-1.129",
pages = "934--940",
abstract = "This paper describes our system used in the SemEval-2023 {\textbackslash}textit{Task 10 Explainable Detection of Online Sexism (EDOS)}. Specifically, we participated in subtask B: a 4-class sexism classification task, and subtask C: a more fine-grained (11-class) sexism classification task, where it is necessary to predict the category of sexism. We treat these two subtasks as one multi-label hierarchical text classification problem, and propose an integrated sexism detection model for improving the performance of the sexism detection task. More concretely, we use the pre-trained BERT model to encode the text and class label and a hierarchy-relevant structure encoder is employed to model the relationship between classes of subtasks B and C. Additionally, a self-training strategy is designed to alleviate the imbalanced problem of distribution classes. Extensive experiments on subtasks B and C demonstrate the effectiveness of our proposed approach.",
}
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<abstract>This paper describes our system used in the SemEval-2023 \textbackslashtextitTask 10 Explainable Detection of Online Sexism (EDOS). Specifically, we participated in subtask B: a 4-class sexism classification task, and subtask C: a more fine-grained (11-class) sexism classification task, where it is necessary to predict the category of sexism. We treat these two subtasks as one multi-label hierarchical text classification problem, and propose an integrated sexism detection model for improving the performance of the sexism detection task. More concretely, we use the pre-trained BERT model to encode the text and class label and a hierarchy-relevant structure encoder is employed to model the relationship between classes of subtasks B and C. Additionally, a self-training strategy is designed to alleviate the imbalanced problem of distribution classes. Extensive experiments on subtasks B and C demonstrate the effectiveness of our proposed approach.</abstract>
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%0 Conference Proceedings
%T HITSZQ at SemEval-2023 Task 10: Category-aware Sexism Detection Model with Self-training Strategy
%A Yao, Ziyi
%A Chai, Heyan
%A Cui, Jinhao
%A Tang, Siyu
%A Liao, Qing
%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 yao-etal-2023-hitszq
%X This paper describes our system used in the SemEval-2023 \textbackslashtextitTask 10 Explainable Detection of Online Sexism (EDOS). Specifically, we participated in subtask B: a 4-class sexism classification task, and subtask C: a more fine-grained (11-class) sexism classification task, where it is necessary to predict the category of sexism. We treat these two subtasks as one multi-label hierarchical text classification problem, and propose an integrated sexism detection model for improving the performance of the sexism detection task. More concretely, we use the pre-trained BERT model to encode the text and class label and a hierarchy-relevant structure encoder is employed to model the relationship between classes of subtasks B and C. Additionally, a self-training strategy is designed to alleviate the imbalanced problem of distribution classes. Extensive experiments on subtasks B and C demonstrate the effectiveness of our proposed approach.
%R 10.18653/v1/2023.semeval-1.129
%U https://aclanthology.org/2023.semeval-1.129
%U https://doi.org/10.18653/v1/2023.semeval-1.129
%P 934-940
Markdown (Informal)
[HITSZQ at SemEval-2023 Task 10: Category-aware Sexism Detection Model with Self-training Strategy](https://aclanthology.org/2023.semeval-1.129) (Yao et al., SemEval 2023)
ACL