@inproceedings{zhong-etal-2023-uirisc,
title = "{UIRISC} at {S}em{E}val-2023 Task 10: Explainable Detection of Online Sexism by Ensembling Fine-tuning Language Models",
author = "Zhong, Tianyun and
Song, Runhui and
Liu, Xunyuan and
Wang, Juelin and
Wang, Boya and
Li, Binyang",
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-1.287",
doi = "10.18653/v1/2023.semeval-1.287",
pages = "2082--2090",
abstract = "Under the umbrella of anonymous social networks, many women have suffered from abuse, discrimination, and other sexist expressions online. However, exsiting methods based on keyword filtering and matching performed poorly on online sexism detection, which lacked the capability to identify implicit stereotypes and discrimination. Therefore, this paper proposes a System of Ensembling Fine-tuning Models (SEFM) at SemEval-2023 Task 10: Explainable Detection of Online Sexism. We firstly use four task-adaptive pre-trained language models to flag all texts. Secondly, we alleviate the data imbalance from two perspectives: over-sampling the labelled data and adjusting the loss function. Thirdly, we add indicators and feedback modules to enhance the overall performance. Our system attained macro F1 scores of 0.8538, 0.6619, and 0.4641 for Subtask A, B, and C, respectively. Our system exhibited strong performance across multiple tasks, with particularly noteworthy performance in Subtask B. Comparison experiments and ablation studies demonstrate the effectiveness of our system.",
}
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<abstract>Under the umbrella of anonymous social networks, many women have suffered from abuse, discrimination, and other sexist expressions online. However, exsiting methods based on keyword filtering and matching performed poorly on online sexism detection, which lacked the capability to identify implicit stereotypes and discrimination. Therefore, this paper proposes a System of Ensembling Fine-tuning Models (SEFM) at SemEval-2023 Task 10: Explainable Detection of Online Sexism. We firstly use four task-adaptive pre-trained language models to flag all texts. Secondly, we alleviate the data imbalance from two perspectives: over-sampling the labelled data and adjusting the loss function. Thirdly, we add indicators and feedback modules to enhance the overall performance. Our system attained macro F1 scores of 0.8538, 0.6619, and 0.4641 for Subtask A, B, and C, respectively. Our system exhibited strong performance across multiple tasks, with particularly noteworthy performance in Subtask B. Comparison experiments and ablation studies demonstrate the effectiveness of our system.</abstract>
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%0 Conference Proceedings
%T UIRISC at SemEval-2023 Task 10: Explainable Detection of Online Sexism by Ensembling Fine-tuning Language Models
%A Zhong, Tianyun
%A Song, Runhui
%A Liu, Xunyuan
%A Wang, Juelin
%A Wang, Boya
%A Li, Binyang
%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 zhong-etal-2023-uirisc
%X Under the umbrella of anonymous social networks, many women have suffered from abuse, discrimination, and other sexist expressions online. However, exsiting methods based on keyword filtering and matching performed poorly on online sexism detection, which lacked the capability to identify implicit stereotypes and discrimination. Therefore, this paper proposes a System of Ensembling Fine-tuning Models (SEFM) at SemEval-2023 Task 10: Explainable Detection of Online Sexism. We firstly use four task-adaptive pre-trained language models to flag all texts. Secondly, we alleviate the data imbalance from two perspectives: over-sampling the labelled data and adjusting the loss function. Thirdly, we add indicators and feedback modules to enhance the overall performance. Our system attained macro F1 scores of 0.8538, 0.6619, and 0.4641 for Subtask A, B, and C, respectively. Our system exhibited strong performance across multiple tasks, with particularly noteworthy performance in Subtask B. Comparison experiments and ablation studies demonstrate the effectiveness of our system.
%R 10.18653/v1/2023.semeval-1.287
%U https://aclanthology.org/2023.semeval-1.287
%U https://doi.org/10.18653/v1/2023.semeval-1.287
%P 2082-2090
Markdown (Informal)
[UIRISC at SemEval-2023 Task 10: Explainable Detection of Online Sexism by Ensembling Fine-tuning Language Models](https://aclanthology.org/2023.semeval-1.287) (Zhong et al., SemEval 2023)
ACL