@inproceedings{mahmoudi-2023-iust,
title = "{IUST}{\_}{NLP} at {S}em{E}val-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining",
author = "Mahmoudi, Hadiseh",
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-1.69",
doi = "10.18653/v1/2023.semeval-1.69",
pages = "498--505",
abstract = "This paper describes our system on SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This work aims to design an automatic system for detecting and classifying sexist content in online spaces. We propose a set of transformer-based pre-trained models with task-adaptive pretraining and ensemble learning. The main contributions of our system include analyzing the performance of different transformer-based pre-trained models and combining these models, as well as providing an efficient method using large amounts of unlabeled data for model adaptive pretraining. We have also explored several other strategies. On the test dataset, our system achieves F1-scores of 83{\%}, 64{\%}, and 47{\%} on subtasks A, B, and C, respectively.",
}
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<abstract>This paper describes our system on SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This work aims to design an automatic system for detecting and classifying sexist content in online spaces. We propose a set of transformer-based pre-trained models with task-adaptive pretraining and ensemble learning. The main contributions of our system include analyzing the performance of different transformer-based pre-trained models and combining these models, as well as providing an efficient method using large amounts of unlabeled data for model adaptive pretraining. We have also explored several other strategies. On the test dataset, our system achieves F1-scores of 83%, 64%, and 47% on subtasks A, B, and C, respectively.</abstract>
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%0 Conference Proceedings
%T IUST_NLP at SemEval-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining
%A Mahmoudi, Hadiseh
%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 mahmoudi-2023-iust
%X This paper describes our system on SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This work aims to design an automatic system for detecting and classifying sexist content in online spaces. We propose a set of transformer-based pre-trained models with task-adaptive pretraining and ensemble learning. The main contributions of our system include analyzing the performance of different transformer-based pre-trained models and combining these models, as well as providing an efficient method using large amounts of unlabeled data for model adaptive pretraining. We have also explored several other strategies. On the test dataset, our system achieves F1-scores of 83%, 64%, and 47% on subtasks A, B, and C, respectively.
%R 10.18653/v1/2023.semeval-1.69
%U https://aclanthology.org/2023.semeval-1.69
%U https://doi.org/10.18653/v1/2023.semeval-1.69
%P 498-505
Markdown (Informal)
[IUST_NLP at SemEval-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining](https://aclanthology.org/2023.semeval-1.69) (Mahmoudi, SemEval 2023)
ACL