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        <title>Cross-Domain Detection of Abusive Language Online</title>
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    <name type="personal">
        <namePart type="given">Mladen</namePart>
        <namePart type="family">Karan</namePart>
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        <namePart type="given">Jan</namePart>
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            <title>Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)</title>
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        <name type="personal">
            <namePart type="given">Darja</namePart>
            <namePart type="family">Fišer</namePart>
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        <name type="personal">
            <namePart type="given">Ruihong</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Vinodkumar</namePart>
            <namePart type="family">Prabhakaran</namePart>
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        <name type="personal">
            <namePart type="given">Rob</namePart>
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        <name type="personal">
            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Waseem</namePart>
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        <name type="personal">
            <namePart type="given">Jacqueline</namePart>
            <namePart type="family">Wernimont</namePart>
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    <abstract>We investigate to what extent the models trained to detect general abusive language generalize between different datasets labeled with different abusive language types. To this end, we compare the cross-domain performance of simple classification models on nine different datasets, finding that the models fail to generalize to out-domain datasets and that having at least some in-domain data is important. We also show that using the frustratingly simple domain adaptation (Daume III, 2007) in most cases improves the results over in-domain training, especially when used to augment a smaller dataset with a larger one.</abstract>
    <identifier type="citekey">karan-snajder-2018-cross</identifier>
    <identifier type="doi">10.18653/v1/W18-5117</identifier>
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        <date>2018-10</date>
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            <start>132</start>
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%0 Conference Proceedings
%T Cross-Domain Detection of Abusive Language Online
%A Karan, Mladen
%A Šnajder, Jan
%Y Fišer, Darja
%Y Huang, Ruihong
%Y Prabhakaran, Vinodkumar
%Y Voigt, Rob
%Y Waseem, Zeerak
%Y Wernimont, Jacqueline
%S Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F karan-snajder-2018-cross
%X We investigate to what extent the models trained to detect general abusive language generalize between different datasets labeled with different abusive language types. To this end, we compare the cross-domain performance of simple classification models on nine different datasets, finding that the models fail to generalize to out-domain datasets and that having at least some in-domain data is important. We also show that using the frustratingly simple domain adaptation (Daume III, 2007) in most cases improves the results over in-domain training, especially when used to augment a smaller dataset with a larger one.
%R 10.18653/v1/W18-5117
%U https://aclanthology.org/W18-5117
%U https://doi.org/10.18653/v1/W18-5117
%P 132-137

Markdown (Informal)

[Cross-Domain Detection of Abusive Language Online](https://aclanthology.org/W18-5117) (Karan & Šnajder, ALW 2018)

• Cross-Domain Detection of Abusive Language Online (Karan & Šnajder, ALW 2018)

ACL

• Mladen Karan and Jan Šnajder. 2018. Cross-Domain Detection of Abusive Language Online. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 132–137, Brussels, Belgium. Association for Computational Linguistics.