SubmissionNumber#=%=#33
FinalPaperTitle#=%=#DetectiveReDASers at HSD-2Lang 2024: A New Pooling Strategy with Cross-lingual Augmentation and Ensembling for Hate Speech Detection in Low-resource Languages
ShortPaperTitle#=%=#
NumberOfPages#=%=#
CopyrightSigned#=%=#
JobTitle#==#
Organization#==#
Abstract#==#This paper addresses hate speech detection in Turkish and Arabic tweets, contributing to the HSD-2Lang Shared Task. We propose a specialized pooling strategy within a soft-voting ensemble framework to improve classification in Turkish and Arabic language models. Our approach also includes expanding the training sets through cross-lingual translation, introducing a broader spectrum of hate speech examples. Our method attains F1-Macro scores of 0.6964 for Turkish (Subtask A) and 0.7123 for Arabic (Subtask B). While achieving these results, we also consider the computational overhead, striking a balance between the effectiveness of our unique pooling strategy, data augmentation, and soft-voting ensemble. This approach advances the practical application of language models in low-resource languages for hate speech detection.
Author{1}{Firstname}#=%=#Fatima Zahra
Author{1}{Lastname}#=%=#Qachfar
Author{1}{Username}#=%=#fqachfar
Author{1}{Email}#=%=#f.qachfar@gmail.com
Author{1}{Affiliation}#=%=#University of Houston
Author{2}{Firstname}#=%=#Bryan
Author{2}{Lastname}#=%=#Tuck
Author{2}{Username}#=%=#tuckb5
Author{2}{Email}#=%=#betuck@cougarnet.uh.edu
Author{2}{Affiliation}#=%=#University of Houston
Author{3}{Firstname}#=%=#Rakesh
Author{3}{Lastname}#=%=#Verma
Author{3}{Username}#=%=#rverma
Author{3}{Email}#=%=#rmverma2@central.uh.edu
Author{3}{Affiliation}#=%=#University of Houston

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