TY - GEN
T1 - HITSZQ at SemEval-2023 Task 10
T2 - 17th International Workshop on Semantic Evaluation, SemEval 2023, co-located with the 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
AU - Yao, Ziyi
AU - Chai, Heyan
AU - Cui, Jinhao
AU - Tang, Siyu
AU - Liao, Qing
N1 - Publisher Copyright:
© 2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - This paper describes our system used in the SemEval-2023 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.
AB - This paper describes our system used in the SemEval-2023 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.
UR - https://www.scopus.com/pages/publications/85175399888
U2 - 10.18653/v1/2023.semeval-1.129
DO - 10.18653/v1/2023.semeval-1.129
M3 - 会议稿件
AN - SCOPUS:85175399888
T3 - 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop
SP - 934
EP - 940
BT - 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop
A2 - Ojha, Atul Kr.
A2 - Dogruoz, A. Seza
A2 - Da San Martino, Giovanni
A2 - Madabushi, Harish Tayyar
A2 - Kumar, Ritesh
A2 - Sartori, Elisa
PB - Association for Computational Linguistics
Y2 - 13 July 2023 through 14 July 2023
ER -