Multiple Voices, Greater Power: A Strategy for Combining Language Models to Combat Hate Speech - Amorim, Annie; Assis, Gabriel; Oliveira, Daniel de; Paes, Aline (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2024.244731, 2024)
Social media platforms face significant issues in avoiding a harmful environment with offensive comments and hate speech. Some of these challenges are inherently linked to the diversity of user perspectives, complicating the classification and detection of hate speech, particularly in culturally rich and diverse countries like Brazil. To address these complexities in identifying hate speech in Brazilian Portuguese, our work proposes the implementation of ensemble methods based on stacking and soft-voting, incorporating four distinct language models with varied architectures and pre-trainings: BERTimbau-base, BERTimbau-large, BERTweet.BR, and Bernice. The findings reveal the superiority of the proposed approach over individual prediction models, suggesting that the combination of multiple models may effectively integrate different perspectives, resulting in an accuracy improvement of up to 6% compared to the isolated classifications of the models.