@inproceedings{s-etal-2021-ssn,
title = "ssn{\_}di{BERT}sity@{LT}-{EDI}-{EACL}2021:Hope Speech Detection on multilingual {Y}ou{T}ube comments via transformer based approach",
author = "S, Arunima and
Ramakrishnan, Akshay and
Balaji, Avantika and
D., Thenmozhi and
B, Senthil Kumar",
editor = "Chakravarthi, Bharathi Raja and
McCrae, John P. and
Zarrouk, Manel and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.ltedi-1.12",
pages = "92--97",
abstract = "In recent times, there exists an abundance of research to classify abusive and offensive texts focusing on negative comments but only minimal research using the positive reinforcement approach. The task was aimed at classifying texts into {`}Hope{\_}speech{'}, {`}Non{\_}hope{\_}speech{'}, and {`}Not in language{'}. The datasets were provided by the LT-EDI organisers in English, Tamil, and Malayalam language with texts sourced from YouTube comments. We trained our data using transformer models, specifically mBERT for Tamil and Malayalam and BERT for English, and achieved weighted average F1-scores of 0.46, 0.81, 0.92 for Tamil, Malayalam, and English respectively.",
}
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<abstract>In recent times, there exists an abundance of research to classify abusive and offensive texts focusing on negative comments but only minimal research using the positive reinforcement approach. The task was aimed at classifying texts into ‘Hope_speech’, ‘Non_hope_speech’, and ‘Not in language’. The datasets were provided by the LT-EDI organisers in English, Tamil, and Malayalam language with texts sourced from YouTube comments. We trained our data using transformer models, specifically mBERT for Tamil and Malayalam and BERT for English, and achieved weighted average F1-scores of 0.46, 0.81, 0.92 for Tamil, Malayalam, and English respectively.</abstract>
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%0 Conference Proceedings
%T ssn_diBERTsity@LT-EDI-EACL2021:Hope Speech Detection on multilingual YouTube comments via transformer based approach
%A S, Arunima
%A Ramakrishnan, Akshay
%A Balaji, Avantika
%A D., Thenmozhi
%A B, Senthil Kumar
%Y Chakravarthi, Bharathi Raja
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv
%F s-etal-2021-ssn
%X In recent times, there exists an abundance of research to classify abusive and offensive texts focusing on negative comments but only minimal research using the positive reinforcement approach. The task was aimed at classifying texts into ‘Hope_speech’, ‘Non_hope_speech’, and ‘Not in language’. The datasets were provided by the LT-EDI organisers in English, Tamil, and Malayalam language with texts sourced from YouTube comments. We trained our data using transformer models, specifically mBERT for Tamil and Malayalam and BERT for English, and achieved weighted average F1-scores of 0.46, 0.81, 0.92 for Tamil, Malayalam, and English respectively.
%U https://aclanthology.org/2021.ltedi-1.12
%P 92-97
Markdown (Informal)
[ssn_diBERTsity@LT-EDI-EACL2021:Hope Speech Detection on multilingual YouTube comments via transformer based approach](https://aclanthology.org/2021.ltedi-1.12) (S et al., LTEDI 2021)
ACL