@inproceedings{singhal-bedi-2024-transformers-lt,
title = "Transformers@{LT}-{EDI}-{EACL}2024: Caste and Migration Hate Speech Detection in {T}amil Using Ensembling on Transformers",
author = "Singhal, Kriti and
Bedi, Jatin",
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-1.32",
pages = "249--253",
abstract = "In recent years, there has been a persistent focus on developing systems that can automatically identify the hate speech content circulating on diverse social media platforms. This paper describes the team {``}Transformers{''} submission to the Caste and Migration Hate Speech Detection in Tamil shared task by LT-EDI 2024 workshop at EACL 2024. We used an ensemble approach in the shared task, combining various transformer-based pre-trained models using majority voting. The best macro average F1-score achieved was 0.82. We secured the 1st rank in the Caste and Migration Hate Speech in Tamil shared task.",
}
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<abstract>In recent years, there has been a persistent focus on developing systems that can automatically identify the hate speech content circulating on diverse social media platforms. This paper describes the team “Transformers” submission to the Caste and Migration Hate Speech Detection in Tamil shared task by LT-EDI 2024 workshop at EACL 2024. We used an ensemble approach in the shared task, combining various transformer-based pre-trained models using majority voting. The best macro average F1-score achieved was 0.82. We secured the 1st rank in the Caste and Migration Hate Speech in Tamil shared task.</abstract>
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%0 Conference Proceedings
%T Transformers@LT-EDI-EACL2024: Caste and Migration Hate Speech Detection in Tamil Using Ensembling on Transformers
%A Singhal, Kriti
%A Bedi, Jatin
%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 singhal-bedi-2024-transformers-lt
%X In recent years, there has been a persistent focus on developing systems that can automatically identify the hate speech content circulating on diverse social media platforms. This paper describes the team “Transformers” submission to the Caste and Migration Hate Speech Detection in Tamil shared task by LT-EDI 2024 workshop at EACL 2024. We used an ensemble approach in the shared task, combining various transformer-based pre-trained models using majority voting. The best macro average F1-score achieved was 0.82. We secured the 1st rank in the Caste and Migration Hate Speech in Tamil shared task.
%U https://aclanthology.org/2024.ltedi-1.32
%P 249-253
Markdown (Informal)
[Transformers@LT-EDI-EACL2024: Caste and Migration Hate Speech Detection in Tamil Using Ensembling on Transformers](https://aclanthology.org/2024.ltedi-1.32) (Singhal & Bedi, LTEDI-WS 2024)
ACL