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        <title>ZYJ123@DravidianLangTech-EACL2021: Offensive Language Identification based on XLM-RoBERTa with DPCNN</title>
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        <namePart type="given">Yingjia</namePart>
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        <namePart type="family">Tao</namePart>
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            <title>Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages</title>
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            <namePart type="given">Bharathi</namePart>
            <namePart type="given">Raja</namePart>
            <namePart type="family">Chakravarthi</namePart>
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            <namePart type="family">Priyadharshini</namePart>
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            <namePart type="given">Anand</namePart>
            <namePart type="family">Kumar M</namePart>
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    <abstract>The development of online media platforms has given users more opportunities to post and comment freely, but the negative impact of offensive language has become increasingly apparent. It is very necessary for the automatic identification system of offensive language. This paper describes our work on the task of Offensive Language Identification in Dravidian language-EACL 2021. To complete this task, we propose a system based on the multilingual model XLM-Roberta and DPCNN. The test results on the official test data set confirm the effectiveness of our system. The weighted average F1-score of Kannada, Malayalam, and Tami language are 0.69, 0.92, and 0.76 respectively, ranked 6th, 6th, and 3rd</abstract>
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%0 Conference Proceedings
%T ZYJ123@DravidianLangTech-EACL2021: Offensive Language Identification based on XLM-RoBERTa with DPCNN
%A Zhao, Yingjia
%A Tao, Xin
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Kumar M, Anand
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%S Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv
%F zhao-tao-2021-zyj123
%X The development of online media platforms has given users more opportunities to post and comment freely, but the negative impact of offensive language has become increasingly apparent. It is very necessary for the automatic identification system of offensive language. This paper describes our work on the task of Offensive Language Identification in Dravidian language-EACL 2021. To complete this task, we propose a system based on the multilingual model XLM-Roberta and DPCNN. The test results on the official test data set confirm the effectiveness of our system. The weighted average F1-score of Kannada, Malayalam, and Tami language are 0.69, 0.92, and 0.76 respectively, ranked 6th, 6th, and 3rd
%U https://aclanthology.org/2021.dravidianlangtech-1.29
%P 216-221

Markdown (Informal)

[ZYJ123@DravidianLangTech-EACL2021: Offensive Language Identification based on XLM-RoBERTa with DPCNN](https://aclanthology.org/2021.dravidianlangtech-1.29) (Zhao & Tao, DravidianLangTech 2021)

• ZYJ123@DravidianLangTech-EACL2021: Offensive Language Identification based on XLM-RoBERTa with DPCNN (Zhao & Tao, DravidianLangTech 2021)

ACL

• Yingjia Zhao and Xin Tao. 2021. ZYJ123@DravidianLangTech-EACL2021: Offensive Language Identification based on XLM-RoBERTa with DPCNN. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, pages 216–221, Kyiv. Association for Computational Linguistics.