@inproceedings{yagci-etal-2024-rebert,
title = "{R}e{BERT} at {HSD}-2{L}ang 2024: Fine-Tuning {BERT} with {A}dam{W} for Hate Speech Detection in {A}rabic and {T}urkish",
author = "Yagci, Utku and
Iscan, Egemen and
Kolcak, Ahmet",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Thapa, Surendrabikram and
Uludo{\u{g}}an, G{\"o}k{\c{c}}e},
booktitle = "Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.case-1.27",
pages = "195--198",
abstract = "Identifying hate speech is a challenging specialization in the natural language processing field (NLP). Particularly in fields with differing linguistics, it becomes more demanding to construct a well-performing classifier for the betterment of the community. In this paper, we leveraged the performances of pre-trained models on the given hate speech detection dataset. By conducting a hyperparameter search, we computed the feasible setups for fine-tuning and trained effective classifiers that performed well in both subtasks in the HSD-2Lang 2024 contest.",
}
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%0 Conference Proceedings
%T ReBERT at HSD-2Lang 2024: Fine-Tuning BERT with AdamW for Hate Speech Detection in Arabic and Turkish
%A Yagci, Utku
%A Iscan, Egemen
%A Kolcak, Ahmet
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Thapa, Surendrabikram
%Y Uludoğan, Gökçe
%S Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F yagci-etal-2024-rebert
%X Identifying hate speech is a challenging specialization in the natural language processing field (NLP). Particularly in fields with differing linguistics, it becomes more demanding to construct a well-performing classifier for the betterment of the community. In this paper, we leveraged the performances of pre-trained models on the given hate speech detection dataset. By conducting a hyperparameter search, we computed the feasible setups for fine-tuning and trained effective classifiers that performed well in both subtasks in the HSD-2Lang 2024 contest.
%U https://aclanthology.org/2024.case-1.27
%P 195-198
Markdown (Informal)
[ReBERT at HSD-2Lang 2024: Fine-Tuning BERT with AdamW for Hate Speech Detection in Arabic and Turkish](https://aclanthology.org/2024.case-1.27) (Yagci et al., CASE-WS 2024)
ACL