Improving hate speech detection using Cross-Lingual Learning

doi:10.1016/j.eswa.2023.121115

Improving hate speech detection using Cross-Lingual Learning

2024-01-01

7

PRE

AI

A

Anderson Almeida Firmino *

C

Cláudio de Souza Baptista

A

Anselmo Cardoso de Paiva

DOI：10.1016/j.eswa.2023.121115

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Abstract

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The growth of social media worldwide has brought social benefits and challenges. One problem we highlight is the proliferation of hate speech on social media. We propose a novel method for detecting hate speech in texts using Cross-Lingual Learning. Our approach uses transfer learning from Pre-Trained Language Models (PTLM) with large corpora available to solve problems in languages with fewer resources for the specific task. The proposed methodology comprises four stages: corpora acquisition, the PTLM definition, training strategies, and evaluation. We carried out experiments using Pre-Trained Language Models in English, Italian, and Portuguese (BERT and XLM-R) to verify which best suited the proposed method. We used corpora in English (WH) and Italian (Evalita 2018) as the source language and the OffComBr-2 corpus in Portuguese (the target language). The results of the experiments showed that the proposed methodology is promising: for the OffComBr-2 corpus, the best state-of-the-art result was obtained (F1-measure = 92%).

Keywords:

Hate speech detection

Natural language processing

Social media

Cross-Lingual Learning

Deep learning

Improving hate speech detection using Cross-Lingual Learning

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