@inproceedings{younes-etal-2023-alexa,
title = "{A}lexa at {S}em{E}val-2023 Task 10: Ensemble Modeling of {D}e{BERT}a and {BERT} Variations for Identifying Sexist Text",
author = "Younes, Mutaz and
Kharabsheh, Ali and
Bani Younes, Mohammad",
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-1.228",
doi = "10.18653/v1/2023.semeval-1.228",
pages = "1644--1649",
abstract = "This study presents an ensemble approach for detecting sexist text in the context of the Semeval-2023 task 10. Our approach leverages 18 models, including DeBERTa-v3-base models with different input sequence lengths, a BERT-based model trained on identifying hate speech, and three more models pre-trained on the task{'}s unlabeled data with varying input lengths. The results of our framework on the development set show an f1-score of 84.92{\%} and on the testing set 84.55{\%}, effectively demonstrating the strength of the ensemble approach in getting accurate results.",
}
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<abstract>This study presents an ensemble approach for detecting sexist text in the context of the Semeval-2023 task 10. Our approach leverages 18 models, including DeBERTa-v3-base models with different input sequence lengths, a BERT-based model trained on identifying hate speech, and three more models pre-trained on the task’s unlabeled data with varying input lengths. The results of our framework on the development set show an f1-score of 84.92% and on the testing set 84.55%, effectively demonstrating the strength of the ensemble approach in getting accurate results.</abstract>
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%0 Conference Proceedings
%T Alexa at SemEval-2023 Task 10: Ensemble Modeling of DeBERTa and BERT Variations for Identifying Sexist Text
%A Younes, Mutaz
%A Kharabsheh, Ali
%A Bani Younes, Mohammad
%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 younes-etal-2023-alexa
%X This study presents an ensemble approach for detecting sexist text in the context of the Semeval-2023 task 10. Our approach leverages 18 models, including DeBERTa-v3-base models with different input sequence lengths, a BERT-based model trained on identifying hate speech, and three more models pre-trained on the task’s unlabeled data with varying input lengths. The results of our framework on the development set show an f1-score of 84.92% and on the testing set 84.55%, effectively demonstrating the strength of the ensemble approach in getting accurate results.
%R 10.18653/v1/2023.semeval-1.228
%U https://aclanthology.org/2023.semeval-1.228
%U https://doi.org/10.18653/v1/2023.semeval-1.228
%P 1644-1649
Markdown (Informal)
[Alexa at SemEval-2023 Task 10: Ensemble Modeling of DeBERTa and BERT Variations for Identifying Sexist Text](https://aclanthology.org/2023.semeval-1.228) (Younes et al., SemEval 2023)
ACL