@inproceedings{alam-etal-2024-cuet,
title = "{CUET}{\_}{NLP}{\_}{M}anning@{LT}-{EDI} 2024: Transformer-based Approach on Caste and Migration Hate Speech Detection",
author = "Alam, Md and
Ali Taher, Hasan Mesbaul and
Hossain, Jawad and
Ahsan, Shawly and
Hoque, Mohammed Moshiul",
editor = {Chakravarthi, Bharathi Raja and
B, Bharathi and
Buitelaar, Paul and
Durairaj, Thenmozhi and
Kov{\'a}cs, Gy{\"o}rgy and
Garc{\'\i}a Cumbreras, Miguel {\'A}ngel},
booktitle = "Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.ltedi-1.30",
pages = "238--243",
abstract = "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 and migration status. Despite several nations efforts to bring equality among their citizens, numerous crimes occur just based on caste. Migration-based hostility happens both in India and in developed countries. A shared task was arranged to address this issue in a low-resourced language such as Tamil. This paper aims to improve the detection of hate speech and hostility based on caste and migration status on social media. To achieve this, this work investigated several Machine Learning (ML), Deep Learning (DL), and transformer-based models, including M-BERT, XLM-R, and Tamil BERT. Experimental results revealed the highest macro f1-score of 0.80 using the M-BERT model, which enabled us to rank 3rd on the shared task.",
}
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<abstract>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 and migration status. Despite several nations efforts to bring equality among their citizens, numerous crimes occur just based on caste. Migration-based hostility happens both in India and in developed countries. A shared task was arranged to address this issue in a low-resourced language such as Tamil. This paper aims to improve the detection of hate speech and hostility based on caste and migration status on social media. To achieve this, this work investigated several Machine Learning (ML), Deep Learning (DL), and transformer-based models, including M-BERT, XLM-R, and Tamil BERT. Experimental results revealed the highest macro f1-score of 0.80 using the M-BERT model, which enabled us to rank 3rd on the shared task.</abstract>
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%0 Conference Proceedings
%T CUET_NLP_Manning@LT-EDI 2024: Transformer-based Approach on Caste and Migration Hate Speech Detection
%A Alam, Md
%A Ali Taher, Hasan Mesbaul
%A Hossain, Jawad
%A Ahsan, Shawly
%A Hoque, Mohammed Moshiul
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Durairaj, Thenmozhi
%Y Kovács, György
%Y García Cumbreras, Miguel Ángel
%S Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F alam-etal-2024-cuet
%X 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 and migration status. Despite several nations efforts to bring equality among their citizens, numerous crimes occur just based on caste. Migration-based hostility happens both in India and in developed countries. A shared task was arranged to address this issue in a low-resourced language such as Tamil. This paper aims to improve the detection of hate speech and hostility based on caste and migration status on social media. To achieve this, this work investigated several Machine Learning (ML), Deep Learning (DL), and transformer-based models, including M-BERT, XLM-R, and Tamil BERT. Experimental results revealed the highest macro f1-score of 0.80 using the M-BERT model, which enabled us to rank 3rd on the shared task.
%U https://aclanthology.org/2024.ltedi-1.30
%P 238-243
Markdown (Informal)
[CUET_NLP_Manning@LT-EDI 2024: Transformer-based Approach on Caste and Migration Hate Speech Detection](https://aclanthology.org/2024.ltedi-1.30) (Alam et al., LTEDI-WS 2024)
ACL