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        <title>Decipherment for Adversarial Offensive Language Detection</title>
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        <namePart type="given">Zhelun</namePart>
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            <title>Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)</title>
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            <namePart type="given">Darja</namePart>
            <namePart type="family">Fišer</namePart>
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        <name type="personal">
            <namePart type="given">Ruihong</namePart>
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            <namePart type="given">Vinodkumar</namePart>
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            <namePart type="given">Jacqueline</namePart>
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    <abstract>Automated filters are commonly used by online services to stop users from sending age-inappropriate, bullying messages, or asking others to expose personal information. Previous work has focused on rules or classifiers to detect and filter offensive messages, but these are vulnerable to cleverly disguised plaintext and unseen expressions especially in an adversarial setting where the users can repeatedly try to bypass the filter. In this paper, we model the disguised messages as if they are produced by encrypting the original message using an invented cipher. We apply automatic decipherment techniques to decode the disguised malicious text, which can be then filtered using rules or classifiers. We provide experimental results on three different datasets and show that decipherment is an effective tool for this task.</abstract>
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%0 Conference Proceedings
%T Decipherment for Adversarial Offensive Language Detection
%A Wu, Zhelun
%A Kambhatla, Nishant
%A Sarkar, Anoop
%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 wu-etal-2018-decipherment
%X Automated filters are commonly used by online services to stop users from sending age-inappropriate, bullying messages, or asking others to expose personal information. Previous work has focused on rules or classifiers to detect and filter offensive messages, but these are vulnerable to cleverly disguised plaintext and unseen expressions especially in an adversarial setting where the users can repeatedly try to bypass the filter. In this paper, we model the disguised messages as if they are produced by encrypting the original message using an invented cipher. We apply automatic decipherment techniques to decode the disguised malicious text, which can be then filtered using rules or classifiers. We provide experimental results on three different datasets and show that decipherment is an effective tool for this task.
%R 10.18653/v1/W18-5119
%U https://aclanthology.org/W18-5119
%U https://doi.org/10.18653/v1/W18-5119
%P 149-159

Markdown (Informal)

[Decipherment for Adversarial Offensive Language Detection](https://aclanthology.org/W18-5119) (Wu et al., ALW 2018)

• Decipherment for Adversarial Offensive Language Detection (Wu et al., ALW 2018)

ACL

• Zhelun Wu, Nishant Kambhatla, and Anoop Sarkar. 2018. Decipherment for Adversarial Offensive Language Detection. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 149–159, Brussels, Belgium. Association for Computational Linguistics.