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    <titleInfo>
        <title>Euphemistic Phrase Detection by Masked Language Model</title>
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    <name type="personal">
        <namePart type="given">Wanzheng</namePart>
        <namePart type="family">Zhu</namePart>
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    <name type="personal">
        <namePart type="given">Suma</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>It is a well-known approach for fringe groups and organizations to use euphemisms—ordinary-sounding and innocent-looking words with a secret meaning—to conceal what they are discussing. For instance, drug dealers often use “pot” for marijuana and “avocado” for heroin. From a social media content moderation perspective, though recent advances in NLP have enabled the automatic detection of such single-word euphemisms, no existing work is capable of automatically detecting multi-word euphemisms, such as “blue dream” (marijuana) and “black tar” (heroin). Our paper tackles the problem of euphemistic phrase detection without human effort for the first time, as far as we are aware. We first perform phrase mining on a raw text corpus (e.g., social media posts) to extract quality phrases. Then, we utilize word embedding similarities to select a set of euphemistic phrase candidates. Finally, we rank those candidates by a masked language model—SpanBERT. Compared to strong baselines, we report 20-50% higher detection accuracies using our algorithm for detecting euphemistic phrases.</abstract>
    <identifier type="citekey">zhu-bhat-2021-euphemistic-phrase</identifier>
    <identifier type="doi">10.18653/v1/2021.findings-emnlp.16</identifier>
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        <date>2021-11</date>
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            <start>163</start>
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%0 Conference Proceedings
%T Euphemistic Phrase Detection by Masked Language Model
%A Zhu, Wanzheng
%A Bhat, Suma
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F zhu-bhat-2021-euphemistic-phrase
%X It is a well-known approach for fringe groups and organizations to use euphemisms—ordinary-sounding and innocent-looking words with a secret meaning—to conceal what they are discussing. For instance, drug dealers often use “pot” for marijuana and “avocado” for heroin. From a social media content moderation perspective, though recent advances in NLP have enabled the automatic detection of such single-word euphemisms, no existing work is capable of automatically detecting multi-word euphemisms, such as “blue dream” (marijuana) and “black tar” (heroin). Our paper tackles the problem of euphemistic phrase detection without human effort for the first time, as far as we are aware. We first perform phrase mining on a raw text corpus (e.g., social media posts) to extract quality phrases. Then, we utilize word embedding similarities to select a set of euphemistic phrase candidates. Finally, we rank those candidates by a masked language model—SpanBERT. Compared to strong baselines, we report 20-50% higher detection accuracies using our algorithm for detecting euphemistic phrases.
%R 10.18653/v1/2021.findings-emnlp.16
%U https://aclanthology.org/2021.findings-emnlp.16
%U https://doi.org/10.18653/v1/2021.findings-emnlp.16
%P 163-168

Markdown (Informal)

[Euphemistic Phrase Detection by Masked Language Model](https://aclanthology.org/2021.findings-emnlp.16) (Zhu & Bhat, Findings 2021)

• Euphemistic Phrase Detection by Masked Language Model (Zhu & Bhat, Findings 2021)

ACL

• Wanzheng Zhu and Suma Bhat. 2021. Euphemistic Phrase Detection by Masked Language Model. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 163–168, Punta Cana, Dominican Republic. Association for Computational Linguistics.