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        <title>cs@DravidianLangTech-EACL2021: Offensive Language Identification Based On Multilingual BERT Model</title>
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        <namePart type="given">Shi</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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        <name type="personal">
            <namePart type="given">Ruba</namePart>
            <namePart type="family">Priyadharshini</namePart>
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            <namePart type="given">Anand</namePart>
            <namePart type="family">Kumar M</namePart>
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            <namePart type="given">Elizabeth</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>This paper introduces the related content of the task “Offensive Language Identification in Dravidian LANGUAGES-EACL 2021”. The task requires us to classify Dravidian languages collected from social media into Not-Offensive, Off-Untargeted, Off-Target-Individual, etc. This data set contains actual annotations in code-mixed text posted by users on Youtube, not from the monolingual text in textbooks. Based on the features of the data set code mixture, we use multilingual BERT and TextCNN for semantic extraction and text classification. In this article, we will show the experiment and result analysis of this task.</abstract>
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%0 Conference Proceedings
%T cs@DravidianLangTech-EACL2021: Offensive Language Identification Based On Multilingual BERT Model
%A Chen, Shi
%A Kong, Bing
%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 chen-kong-2021-cs
%X This paper introduces the related content of the task “Offensive Language Identification in Dravidian LANGUAGES-EACL 2021”. The task requires us to classify Dravidian languages collected from social media into Not-Offensive, Off-Untargeted, Off-Target-Individual, etc. This data set contains actual annotations in code-mixed text posted by users on Youtube, not from the monolingual text in textbooks. Based on the features of the data set code mixture, we use multilingual BERT and TextCNN for semantic extraction and text classification. In this article, we will show the experiment and result analysis of this task.
%U https://aclanthology.org/2021.dravidianlangtech-1.31/
%P 230-235

Markdown (Informal)

[cs@DravidianLangTech-EACL2021: Offensive Language Identification Based On Multilingual BERT Model](https://aclanthology.org/2021.dravidianlangtech-1.31/) (Chen & Kong, DravidianLangTech 2021)

• cs@DravidianLangTech-EACL2021: Offensive Language Identification Based On Multilingual BERT Model (Chen & Kong, DravidianLangTech 2021)

ACL

• Shi Chen and Bing Kong. 2021. cs@DravidianLangTech-EACL2021: Offensive Language Identification Based On Multilingual BERT Model. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, pages 230–235, Kyiv. Association for Computational Linguistics.