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    <titleInfo>
        <title>Explainable Abuse Detection as Intent Classification and Slot Filling</title>
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    <name type="personal">
        <namePart type="given">Agostina</namePart>
        <namePart type="family">Calabrese</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Björn</namePart>
        <namePart type="family">Ross</namePart>
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    <name type="personal">
        <namePart type="given">Mirella</namePart>
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    <abstract>To proactively offer social media users a safe online experience, there is a need for systems that can detect harmful posts and promptly alert platform moderators. In order to guarantee the enforcement of a consistent policy, moderators are provided with detailed guidelines. In contrast, most state-of-the-art models learn what abuse is from labeled examples and as a result base their predictions on spurious cues, such as the presence of group identifiers, which can be unreliable. In this work we introduce the concept of policy-aware abuse detection, abandoning the unrealistic expectation that systems can reliably learn which phenomena constitute abuse from inspecting the data alone. We propose a machine-friendly representation of the policy that moderators wish to enforce, by breaking it down into a collection of intents and slots. We collect and annotate a dataset of 3,535 English posts with such slots, and show how architectures for intent classification and slot filling can be used for abuse detection, while providing a rationale for model decisions.1</abstract>
    <identifier type="citekey">calabrese-etal-2022-explainable</identifier>
    <identifier type="doi">10.1162/tacl_a_00527</identifier>
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        <url>https://aclanthology.org/2022.tacl-1.82</url>
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    <part>
        <date>2022</date>
        <detail type="volume"><number>10</number></detail>
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            <start>1440</start>
            <end>1454</end>
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%0 Journal Article
%T Explainable Abuse Detection as Intent Classification and Slot Filling
%A Calabrese, Agostina
%A Ross, Björn
%A Lapata, Mirella
%J Transactions of the Association for Computational Linguistics
%D 2022
%V 10
%I MIT Press
%C Cambridge, MA
%F calabrese-etal-2022-explainable
%X To proactively offer social media users a safe online experience, there is a need for systems that can detect harmful posts and promptly alert platform moderators. In order to guarantee the enforcement of a consistent policy, moderators are provided with detailed guidelines. In contrast, most state-of-the-art models learn what abuse is from labeled examples and as a result base their predictions on spurious cues, such as the presence of group identifiers, which can be unreliable. In this work we introduce the concept of policy-aware abuse detection, abandoning the unrealistic expectation that systems can reliably learn which phenomena constitute abuse from inspecting the data alone. We propose a machine-friendly representation of the policy that moderators wish to enforce, by breaking it down into a collection of intents and slots. We collect and annotate a dataset of 3,535 English posts with such slots, and show how architectures for intent classification and slot filling can be used for abuse detection, while providing a rationale for model decisions.1
%R 10.1162/tacl_a_00527
%U https://aclanthology.org/2022.tacl-1.82
%U https://doi.org/10.1162/tacl_a_00527
%P 1440-1454

Markdown (Informal)

[Explainable Abuse Detection as Intent Classification and Slot Filling](https://aclanthology.org/2022.tacl-1.82) (Calabrese et al., TACL 2022)

• Explainable Abuse Detection as Intent Classification and Slot Filling (Calabrese et al., TACL 2022)

ACL

• Agostina Calabrese, Björn Ross, and Mirella Lapata. 2022. Explainable Abuse Detection as Intent Classification and Slot Filling. Transactions of the Association for Computational Linguistics, 10:1440–1454.