@inproceedings{kavatagi-etal-2023-vtubgm,
title = "{VTUBGM}@{LT}-{EDI}-2023: Hope Speech Identification using Layered Differential Training of {ULMF}it",
author = "Kavatagi, Sanjana M. and
Rachh, Rashmi R. and
Biradar, Shankar S.",
editor = "Chakravarthi, Bharathi R. and
Bharathi, B. and
Griffith, Joephine and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ltedi-1.32/",
pages = "209--213",
abstract = "Hope speech embodies optimistic and uplifting sentiments, aiming to inspire individuals to maintain faith in positive progress and actively contribute to a better future. In this article, we outline the model presented by our team, VTUBGM, for the shared task {\textquotedblleft}Hope Speech Detection for Equality, Diversity, and Inclusion{\textquotedblright} at LT-EDI-RANLP 2023. This task entails classifying YouTube comments, which is a classification problem at the comment level. The task was conducted in four different languages: Bulgarian, English, Hindi, and Spanish. VTUBGM submitted a model developed through layered differential training of the ULMFit model. As a result, a macro F1 score of 0.48 was obtained and ranked 3rd in the competition."
}
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<abstract>Hope speech embodies optimistic and uplifting sentiments, aiming to inspire individuals to maintain faith in positive progress and actively contribute to a better future. In this article, we outline the model presented by our team, VTUBGM, for the shared task “Hope Speech Detection for Equality, Diversity, and Inclusion” at LT-EDI-RANLP 2023. This task entails classifying YouTube comments, which is a classification problem at the comment level. The task was conducted in four different languages: Bulgarian, English, Hindi, and Spanish. VTUBGM submitted a model developed through layered differential training of the ULMFit model. As a result, a macro F1 score of 0.48 was obtained and ranked 3rd in the competition.</abstract>
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%0 Conference Proceedings
%T VTUBGM@LT-EDI-2023: Hope Speech Identification using Layered Differential Training of ULMFit
%A Kavatagi, Sanjana M.
%A Rachh, Rashmi R.
%A Biradar, Shankar S.
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F kavatagi-etal-2023-vtubgm
%X Hope speech embodies optimistic and uplifting sentiments, aiming to inspire individuals to maintain faith in positive progress and actively contribute to a better future. In this article, we outline the model presented by our team, VTUBGM, for the shared task “Hope Speech Detection for Equality, Diversity, and Inclusion” at LT-EDI-RANLP 2023. This task entails classifying YouTube comments, which is a classification problem at the comment level. The task was conducted in four different languages: Bulgarian, English, Hindi, and Spanish. VTUBGM submitted a model developed through layered differential training of the ULMFit model. As a result, a macro F1 score of 0.48 was obtained and ranked 3rd in the competition.
%U https://aclanthology.org/2023.ltedi-1.32/
%P 209-213
Markdown (Informal)
[VTUBGM@LT-EDI-2023: Hope Speech Identification using Layered Differential Training of ULMFit](https://aclanthology.org/2023.ltedi-1.32/) (Kavatagi et al., LTEDI 2023)
ACL