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        <title>Simon @ DravidianLangTech-EACL2021: Detecting Offensive Content in Kannada Language</title>
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            <namePart type="given">Bharathi</namePart>
            <namePart type="given">Raja</namePart>
            <namePart type="family">Chakravarthi</namePart>
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    <abstract>This article introduces the system for the shared task of Offensive Language Identification in Dravidian Languages-EACL 2021. The world‘s information technology develops at a high speed. People are used to expressing their views and opinions on social media. This leads to a lot of offensive language on social media. As people become more dependent on social media, the detection of offensive language becomes more and more necessary. This shared task is in three languages: Tamil, Malayalam, and Kannada. Our team takes part in the Kannada language task. To accomplish the task, we use the XLM-Roberta model for pre-training. But the capabilities of the XLM-Roberta model do not satisfy us in terms of statement information collection. So we made some tweaks to the output of this model. In this paper, we describe the models and experiments for accomplishing the task of the Kannada language.</abstract>
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%0 Conference Proceedings
%T Simon @ DravidianLangTech-EACL2021: Detecting Offensive Content in Kannada Language
%A Que, Qinyu
%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 que-2021-simon
%X This article introduces the system for the shared task of Offensive Language Identification in Dravidian Languages-EACL 2021. The world‘s information technology develops at a high speed. People are used to expressing their views and opinions on social media. This leads to a lot of offensive language on social media. As people become more dependent on social media, the detection of offensive language becomes more and more necessary. This shared task is in three languages: Tamil, Malayalam, and Kannada. Our team takes part in the Kannada language task. To accomplish the task, we use the XLM-Roberta model for pre-training. But the capabilities of the XLM-Roberta model do not satisfy us in terms of statement information collection. So we made some tweaks to the output of this model. In this paper, we describe the models and experiments for accomplishing the task of the Kannada language.
%U https://aclanthology.org/2021.dravidianlangtech-1.20/
%P 160-163

Markdown (Informal)

[Simon @ DravidianLangTech-EACL2021: Detecting Offensive Content in Kannada Language](https://aclanthology.org/2021.dravidianlangtech-1.20/) (Que, DravidianLangTech 2021)

• Simon @ DravidianLangTech-EACL2021: Detecting Offensive Content in Kannada Language (Que, DravidianLangTech 2021)

ACL

• Qinyu Que. 2021. Simon @ DravidianLangTech-EACL2021: Detecting Offensive Content in Kannada Language. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, pages 160–163, Kyiv. Association for Computational Linguistics.