Autores
Shahiki Tash Moein
Ahani Zahra
Zamir Muhammad Tayyab
Kolesnikova Olga
Sidorov Grigori
Título Lidoma@LT-EDI 2024:Tamil Hate Speech Detection in Migration Discourse
Tipo Congreso
Sub-tipo Memoria
Descripción 4th Workshop on Language Technology for Equality, Diversity, Inclusion, LT-EDI 2024
Resumen The exponential rise in social media users has revolutionized information accessibility and exchange. While these platforms serve various purposes, they also harbor negative elements, including hate speech and offensive behavior. Detecting hate speech in diverse languages has garnered significant attention in Natural Language Processing (NLP). This paper delves into hate speech detection in Tamil, particularly related to migration and refuge, contributing to the Caste/migration hate speech detection shared task. Employing a Convolutional Neural Network (CNN), our model achieved an F1 score of 0.76 in identifying hate speech and signaling potential in the domain despite encountering complexities. We provide an overview of related research, methodology, and insights into the competition’s diverse performances, showcasing the landscape of hate speech detection nuances in the Tamil language. © 2024 Association for Computational Linguistics.
Observaciones
Lugar St. Julians
País Malta
No. de páginas 184-189
Vol. / Cap.
Inicio 2024-03-22
Fin
ISBN/ISSN 9798891760813