@inproceedings{muti-etal-2023-uniboes,
title = "{U}ni{B}oe{'}s at {S}em{E}val-2023 Task 10: Model-Agnostic Strategies for the Improvement of Hate-Tuned and Generative Models in the Classification of Sexist Posts",
author = "Muti, Arianna and
Fernicola, Francesco and
Barr{\'o}n-Cede{\~n}o, Alberto",
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-1.158",
doi = "10.18653/v1/2023.semeval-1.158",
pages = "1138--1147",
abstract = "We present our submission to SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). We address all three tasks: Task A consists of identifying whether a post is sexist. If so, Task B attempts to assign it one of four categories: threats, derogation, animosity, and prejudiced discussions. Task C aims for an even more fine-grained classification, divided among 11 classes. Our team UniBoe{'}s experiments with fine-tuning of hate-tuned Transformer-based models and priming for generative models. In addition, we explore model-agnostic strategies, such as data augmentation techniques combined with active learning, as well as obfuscation of identity terms. Our official submissions obtain an F1{\_}score of 0.83 for Task A, 0.58 for Task B and 0.32 for Task C.",
}
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<abstract>We present our submission to SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). We address all three tasks: Task A consists of identifying whether a post is sexist. If so, Task B attempts to assign it one of four categories: threats, derogation, animosity, and prejudiced discussions. Task C aims for an even more fine-grained classification, divided among 11 classes. Our team UniBoe’s experiments with fine-tuning of hate-tuned Transformer-based models and priming for generative models. In addition, we explore model-agnostic strategies, such as data augmentation techniques combined with active learning, as well as obfuscation of identity terms. Our official submissions obtain an F1_score of 0.83 for Task A, 0.58 for Task B and 0.32 for Task C.</abstract>
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%0 Conference Proceedings
%T UniBoe’s at SemEval-2023 Task 10: Model-Agnostic Strategies for the Improvement of Hate-Tuned and Generative Models in the Classification of Sexist Posts
%A Muti, Arianna
%A Fernicola, Francesco
%A Barrón-Cedeño, Alberto
%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 muti-etal-2023-uniboes
%X We present our submission to SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). We address all three tasks: Task A consists of identifying whether a post is sexist. If so, Task B attempts to assign it one of four categories: threats, derogation, animosity, and prejudiced discussions. Task C aims for an even more fine-grained classification, divided among 11 classes. Our team UniBoe’s experiments with fine-tuning of hate-tuned Transformer-based models and priming for generative models. In addition, we explore model-agnostic strategies, such as data augmentation techniques combined with active learning, as well as obfuscation of identity terms. Our official submissions obtain an F1_score of 0.83 for Task A, 0.58 for Task B and 0.32 for Task C.
%R 10.18653/v1/2023.semeval-1.158
%U https://aclanthology.org/2023.semeval-1.158
%U https://doi.org/10.18653/v1/2023.semeval-1.158
%P 1138-1147
Markdown (Informal)
[UniBoe’s at SemEval-2023 Task 10: Model-Agnostic Strategies for the Improvement of Hate-Tuned and Generative Models in the Classification of Sexist Posts](https://aclanthology.org/2023.semeval-1.158) (Muti et al., SemEval 2023)
ACL