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        <title>hate-alert@LT-EDI-2023: Hope Speech Detection Using Transformer-Based Models</title>
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        <namePart type="given">Mithun</namePart>
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            <namePart type="given">Bharathi</namePart>
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            <namePart type="family">Chakravarthi</namePart>
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    <abstract>Social media platforms have become integral to our daily lives, facilitating instant sharing of thoughts and ideas. While these platforms often host inspiring, motivational, and positive content, the research community has recognized the significance of such messages by labeling them as “hope speech”. In light of this, we delve into the detection of hope speech on social media platforms. Specifically, we explore various transformer-based model setups for the LT-EDI shared task at RANLP 2023. We observe that the performance of the models varies across languages. Overall, the finetuned m-BERT model showcases the best performance among all the models across languages. Our models secured the first position in Bulgarian and Hindi languages and achieved the third position for the Spanish language in the respective task.</abstract>
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%0 Conference Proceedings
%T hate-alert@LT-EDI-2023: Hope Speech Detection Using Transformer-Based Models
%A Das, Mithun
%A Barman, Shubhankar
%A Chatterjee, Subhadeep
%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 das-etal-2023-hate
%X Social media platforms have become integral to our daily lives, facilitating instant sharing of thoughts and ideas. While these platforms often host inspiring, motivational, and positive content, the research community has recognized the significance of such messages by labeling them as “hope speech”. In light of this, we delve into the detection of hope speech on social media platforms. Specifically, we explore various transformer-based model setups for the LT-EDI shared task at RANLP 2023. We observe that the performance of the models varies across languages. Overall, the finetuned m-BERT model showcases the best performance among all the models across languages. Our models secured the first position in Bulgarian and Hindi languages and achieved the third position for the Spanish language in the respective task.
%U https://aclanthology.org/2023.ltedi-1.38
%P 250-256

Markdown (Informal)

[hate-alert@LT-EDI-2023: Hope Speech Detection Using Transformer-Based Models](https://aclanthology.org/2023.ltedi-1.38) (Das et al., LTEDI-WS 2023)

• hate-alert@LT-EDI-2023: Hope Speech Detection Using Transformer-Based Models (Das et al., LTEDI-WS 2023)

ACL

• Mithun Das, Shubhankar Barman, and Subhadeep Chatterjee. 2023. hate-alert@LT-EDI-2023: Hope Speech Detection Using Transformer-Based Models. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 250–256, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.