@inproceedings{lamsiyah-etal-2023-ul,
title = "{UL} {\&} {UM}6{P} at {S}em{E}val-2023 Task 10: Semi-Supervised Multi-task Learning for Explainable Detection of Online Sexism",
author = "Lamsiyah, Salima and
El Mahdaouy, Abdelkader and
Alami, Hamza and
Berrada, Ismail and
Schommer, Christoph",
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-1.88",
doi = "10.18653/v1/2023.semeval-1.88",
pages = "644--650",
abstract = "This paper introduces our participating system to the Explainable Detection of Online Sexism (EDOS) SemEval-2023 - Task 10: Explainable Detection of Online Sexism. The EDOS shared task covers three hierarchical sub-tasks for sexism detection, coarse-grained and fine-grained categorization. We have investigated both single-task and multi-task learning based on RoBERTa transformer-based language models. For improving the results, we have performed further pre-training of RoBERTa on the provided unlabeled data. Besides, we have employed a small sample of the unlabeled data for semi-supervised learning using the minimum class-confusion loss. Our system has achieved macro F1 scores of 82.25{\textbackslash}{\%}, 67.35{\textbackslash}{\%}, and 49.8{\textbackslash}{\%} on Tasks A, B, and C, respectively.",
}
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<abstract>This paper introduces our participating system to the Explainable Detection of Online Sexism (EDOS) SemEval-2023 - Task 10: Explainable Detection of Online Sexism. The EDOS shared task covers three hierarchical sub-tasks for sexism detection, coarse-grained and fine-grained categorization. We have investigated both single-task and multi-task learning based on RoBERTa transformer-based language models. For improving the results, we have performed further pre-training of RoBERTa on the provided unlabeled data. Besides, we have employed a small sample of the unlabeled data for semi-supervised learning using the minimum class-confusion loss. Our system has achieved macro F1 scores of 82.25\textbackslash%, 67.35\textbackslash%, and 49.8\textbackslash% on Tasks A, B, and C, respectively.</abstract>
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%0 Conference Proceedings
%T UL & UM6P at SemEval-2023 Task 10: Semi-Supervised Multi-task Learning for Explainable Detection of Online Sexism
%A Lamsiyah, Salima
%A El Mahdaouy, Abdelkader
%A Alami, Hamza
%A Berrada, Ismail
%A Schommer, Christoph
%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 lamsiyah-etal-2023-ul
%X This paper introduces our participating system to the Explainable Detection of Online Sexism (EDOS) SemEval-2023 - Task 10: Explainable Detection of Online Sexism. The EDOS shared task covers three hierarchical sub-tasks for sexism detection, coarse-grained and fine-grained categorization. We have investigated both single-task and multi-task learning based on RoBERTa transformer-based language models. For improving the results, we have performed further pre-training of RoBERTa on the provided unlabeled data. Besides, we have employed a small sample of the unlabeled data for semi-supervised learning using the minimum class-confusion loss. Our system has achieved macro F1 scores of 82.25\textbackslash%, 67.35\textbackslash%, and 49.8\textbackslash% on Tasks A, B, and C, respectively.
%R 10.18653/v1/2023.semeval-1.88
%U https://aclanthology.org/2023.semeval-1.88
%U https://doi.org/10.18653/v1/2023.semeval-1.88
%P 644-650
Markdown (Informal)
[UL & UM6P at SemEval-2023 Task 10: Semi-Supervised Multi-task Learning for Explainable Detection of Online Sexism](https://aclanthology.org/2023.semeval-1.88) (Lamsiyah et al., SemEval 2023)
ACL