Work

ReBERT at HSD-2Lang 2024: Fine-Tuning BERT with AdamW for Hate Speech Detection in Arabic and Turkish

Year: 2024

Type: conference-paper

Abstract: This research tackles the issue of detecting hate speech in Arabic and Turkish languages by utilizing pre-trained BERT models, namely Turk-ishBERTweet and Arabertv02-twitter.These models are enhanced through a comprehensive hyperparameter search to improve their performance.Our classifiers excelled in the HSD-2Lang 2024 contest, with the Turkish model placing second in Subtask A and the Arabic model first in Subtask B on the private leaderboard.Both models also ranked first on the public dataset.These results demonstrate the efficacy and adaptability of our approach in addressing the evolving challenges of hate speech detection in multilingual contexts.

Source: Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)

DOI: 10.18653/v1/2024.case-1.27

Authors: Utku Yagci, Egemen Iscan, Ahmet Kolcak

Institutions: Middle East Technical University, King's College London, Istanbul Technical University

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7126439227

FWCI: 0.885

Cites:

Cited by: 3

Related to: 10