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        <title>NITK-IT-NLP@DravidianLangTech: Impact of Focal Loss on Malayalam Fake News Detection using Transformers</title>
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    <name type="personal">
        <namePart type="given">Hariharan</namePart>
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        <namePart type="given">Anand</namePart>
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            <title>Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages</title>
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            <namePart type="given">Bharathi</namePart>
            <namePart type="given">R</namePart>
            <namePart type="family">Chakravarthi</namePart>
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            <namePart type="given">Ruba</namePart>
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            <namePart type="given">Sajeetha</namePart>
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    <abstract>Fake News Detection in Dravidian Languages is a shared task that identifies youtube comments in the Malayalam language for fake news detection. In this work, we have proposed a transformer-based model with cross-entropy loss and focal loss, which classifies the comments into fake or authentic news. We have used different transformer-based models for the dataset with modifications in the experimental setup, out of which the fine-tuned model, which is based on MuRIL with focal loss, achieved the best overall macro F1-score of 0.87, and we got second position in the final leaderboard.</abstract>
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%0 Conference Proceedings
%T NITK-IT-NLP@DravidianLangTech: Impact of Focal Loss on Malayalam Fake News Detection using Transformers
%A R L, Hariharan
%A M, Anand Kumar
%Y Chakravarthi, Bharathi R.
%Y Priyadharshini, Ruba
%Y M, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%S Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F r-l-m-2023-nitk
%X Fake News Detection in Dravidian Languages is a shared task that identifies youtube comments in the Malayalam language for fake news detection. In this work, we have proposed a transformer-based model with cross-entropy loss and focal loss, which classifies the comments into fake or authentic news. We have used different transformer-based models for the dataset with modifications in the experimental setup, out of which the fine-tuned model, which is based on MuRIL with focal loss, achieved the best overall macro F1-score of 0.87, and we got second position in the final leaderboard.
%U https://aclanthology.org/2023.dravidianlangtech-1.29
%P 207-210

Markdown (Informal)

[NITK-IT-NLP@DravidianLangTech: Impact of Focal Loss on Malayalam Fake News Detection using Transformers](https://aclanthology.org/2023.dravidianlangtech-1.29) (R L & M, DravidianLangTech-WS 2023)

• NITK-IT-NLP@DravidianLangTech: Impact of Focal Loss on Malayalam Fake News Detection using Transformers (R L & M, DravidianLangTech-WS 2023)

ACL

• Hariharan R L and Anand Kumar M. 2023. NITK-IT-NLP@DravidianLangTech: Impact of Focal Loss on Malayalam Fake News Detection using Transformers. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 207–210, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.