SubmissionNumber#=%=#32
FinalPaperTitle#=%=#ReBERT at HSD-2Lang 2024: Fine-Tuning BERT with AdamW for Hate Speech Detection in Arabic and Turkish
ShortPaperTitle#=%=#
NumberOfPages#=%=#
CopyrightSigned#=%=#
JobTitle#==#
Organization#==#
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.
Author{1}{Firstname}#=%=#Utku
Author{1}{Lastname}#=%=#Yagci
Author{1}{Username}#=%=#utkuyagci
Author{1}{Email}#=%=#utkuuguryagci07@gmail.com
Author{1}{Affiliation}#=%=#Middle East Technical University
Author{2}{Firstname}#=%=#Egemen
Author{2}{Lastname}#=%=#Iscan
Author{2}{Email}#=%=#egemen.iscan@kcl.ac.uk
Author{2}{Affiliation}#=%=#King's Business School
Author{3}{Firstname}#=%=#Ahmet Emirhan
Author{3}{Lastname}#=%=#Kolcak
Author{3}{Email}#=%=#kolcak20@itu.edu.tr
Author{3}{Affiliation}#=%=#Istanbul Technical University

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