@inproceedings{b-etal-2022-ssncse,
title = "{SSNCSE}{\_}{NLP}@{LT}-{EDI}-{ACL}2022:Hope Speech Detection for Equality, Diversity and Inclusion using sentence transformers",
author = "B, Bharathi and
Srinivasan, Dhanya and
Varsha, Josephine and
Durairaj, Thenmozhi and
B, Senthil Kumar",
editor = "Chakravarthi, Bharathi Raja and
Bharathi, B and
McCrae, John P and
Zarrouk, Manel and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ltedi-1.30",
doi = "10.18653/v1/2022.ltedi-1.30",
pages = "218--222",
abstract = "In recent times, applications have been developed to regulate and control the spread of negativity and toxicity on online platforms. The world is filled with serious problems like political {\&} religious conflicts, wars, pandemics, and offensive hate speech is the last thing we desire. Our task was to classify a text into {`}Hope Speech{'} and {`}Non-Hope Speech{'}. We searched for datasets acquired from YouTube comments that offer support, reassurance, inspiration, and insight, and the ones that don{'}t. The datasets were provided to us by the LTEDI organizers in English, Tamil, Spanish, Kannada, and Malayalam. To successfully identify and classify them, we employed several machine learning transformer models such as m-BERT, MLNet, BERT, XLMRoberta, and XLM{\_}MLM. The observed results indicate that the BERT and m-BERT have obtained the best results among all the other techniques, gaining a weighted F1- score of 0.92, 0.71, 0.76, 0.87, and 0.83 for English, Tamil, Spanish, Kannada, and Malayalam respectively. This paper depicts our work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LTEDI 2021.",
}
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<abstract>In recent times, applications have been developed to regulate and control the spread of negativity and toxicity on online platforms. The world is filled with serious problems like political & religious conflicts, wars, pandemics, and offensive hate speech is the last thing we desire. Our task was to classify a text into ‘Hope Speech’ and ‘Non-Hope Speech’. We searched for datasets acquired from YouTube comments that offer support, reassurance, inspiration, and insight, and the ones that don’t. The datasets were provided to us by the LTEDI organizers in English, Tamil, Spanish, Kannada, and Malayalam. To successfully identify and classify them, we employed several machine learning transformer models such as m-BERT, MLNet, BERT, XLMRoberta, and XLM_MLM. The observed results indicate that the BERT and m-BERT have obtained the best results among all the other techniques, gaining a weighted F1- score of 0.92, 0.71, 0.76, 0.87, and 0.83 for English, Tamil, Spanish, Kannada, and Malayalam respectively. This paper depicts our work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LTEDI 2021.</abstract>
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%0 Conference Proceedings
%T SSNCSE_NLP@LT-EDI-ACL2022:Hope Speech Detection for Equality, Diversity and Inclusion using sentence transformers
%A B, Bharathi
%A Srinivasan, Dhanya
%A Varsha, Josephine
%A Durairaj, Thenmozhi
%A B, Senthil Kumar
%Y Chakravarthi, Bharathi Raja
%Y Bharathi, B.
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F b-etal-2022-ssncse
%X In recent times, applications have been developed to regulate and control the spread of negativity and toxicity on online platforms. The world is filled with serious problems like political & religious conflicts, wars, pandemics, and offensive hate speech is the last thing we desire. Our task was to classify a text into ‘Hope Speech’ and ‘Non-Hope Speech’. We searched for datasets acquired from YouTube comments that offer support, reassurance, inspiration, and insight, and the ones that don’t. The datasets were provided to us by the LTEDI organizers in English, Tamil, Spanish, Kannada, and Malayalam. To successfully identify and classify them, we employed several machine learning transformer models such as m-BERT, MLNet, BERT, XLMRoberta, and XLM_MLM. The observed results indicate that the BERT and m-BERT have obtained the best results among all the other techniques, gaining a weighted F1- score of 0.92, 0.71, 0.76, 0.87, and 0.83 for English, Tamil, Spanish, Kannada, and Malayalam respectively. This paper depicts our work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LTEDI 2021.
%R 10.18653/v1/2022.ltedi-1.30
%U https://aclanthology.org/2022.ltedi-1.30
%U https://doi.org/10.18653/v1/2022.ltedi-1.30
%P 218-222
Markdown (Informal)
[SSNCSE_NLP@LT-EDI-ACL2022:Hope Speech Detection for Equality, Diversity and Inclusion using sentence transformers](https://aclanthology.org/2022.ltedi-1.30) (B et al., LTEDI 2022)
ACL