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CUET_NLP_Manning_LT-EDI@EACL2024: Transformer Based Approach on Caste or Immigration Hate Speech Detection

Domaine:

natural language processing

Type de record:

paper
Créateur:
AssAhsAlaAli
Éditeur:
Und
Hôte:avatar
The widespread use of online communication has caused a significant increase in the spread of hate speech on social media. However, there are also hate crimes based on caste or immigration status. Despite India's efforts to bring equality among its citizens, numerous crimes occur just based on caste. Immigration-based hostility happens both in India and in developed countries. A shared task was arranged to address this issue in a low-resource language such as Tamil. This paper aims to improve the detection of hate speech and hostility based on caste and immigration status on social media. To achieve this, we investigated several Machine Learning (ML), Deep Learning (DL), and transformer-based models, including M-BERT, XLM-R, and Tamil BERT. We obtained a macro -score of 0.80 using the M-BERT model, which enabled us to rank on the shared task.

Visit

doi.orgunderline.io

Tasks

hate speech detectiontext classification

Tags

Computational LinguisticsNatural Language Processing

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