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        <title>Pay “Attention” to your Context when Classifying Abusive Language</title>
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    <name type="personal">
        <namePart type="given">Tuhin</namePart>
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    <name type="personal">
        <namePart type="given">Smaranda</namePart>
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            <title>Proceedings of the Third Workshop on Abusive Language Online</title>
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            <namePart type="given">Sarah</namePart>
            <namePart type="given">T</namePart>
            <namePart type="family">Roberts</namePart>
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            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Vinodkumar</namePart>
            <namePart type="family">Prabhakaran</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Waseem</namePart>
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    <abstract>The goal of any social media platform is to facilitate healthy and meaningful interactions among its users. But more often than not, it has been found that it becomes an avenue for wanton attacks. We propose an experimental study that has three aims: 1) to provide us with a deeper understanding of current data sets that focus on different types of abusive language, which are sometimes overlapping (racism, sexism, hate speech, offensive language, and personal attacks); 2) to investigate what type of attention mechanism (contextual vs. self-attention) is better for abusive language detection using deep learning architectures; and 3) to investigate whether stacked architectures provide an advantage over simple architectures for this task.</abstract>
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%0 Conference Proceedings
%T Pay “Attention” to your Context when Classifying Abusive Language
%A Chakrabarty, Tuhin
%A Gupta, Kilol
%A Muresan, Smaranda
%Y Roberts, Sarah T.
%Y Tetreault, Joel
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the Third Workshop on Abusive Language Online
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F chakrabarty-etal-2019-pay
%X The goal of any social media platform is to facilitate healthy and meaningful interactions among its users. But more often than not, it has been found that it becomes an avenue for wanton attacks. We propose an experimental study that has three aims: 1) to provide us with a deeper understanding of current data sets that focus on different types of abusive language, which are sometimes overlapping (racism, sexism, hate speech, offensive language, and personal attacks); 2) to investigate what type of attention mechanism (contextual vs. self-attention) is better for abusive language detection using deep learning architectures; and 3) to investigate whether stacked architectures provide an advantage over simple architectures for this task.
%R 10.18653/v1/W19-3508
%U https://aclanthology.org/W19-3508
%U https://doi.org/10.18653/v1/W19-3508
%P 70-79

Markdown (Informal)

[Pay “Attention” to your Context when Classifying Abusive Language](https://aclanthology.org/W19-3508) (Chakrabarty et al., ALW 2019)

• Pay “Attention” to your Context when Classifying Abusive Language (Chakrabarty et al., ALW 2019)

ACL

• Tuhin Chakrabarty, Kilol Gupta, and Smaranda Muresan. 2019. Pay “Attention” to your Context when Classifying Abusive Language. In Proceedings of the Third Workshop on Abusive Language Online, pages 70–79, Florence, Italy. Association for Computational Linguistics.