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        <title>Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language</title>
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        <namePart type="given">Michael</namePart>
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    <name type="personal">
        <namePart type="given">Josef</namePart>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Houda</namePart>
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    <abstract>We address the task of identifying euphemistic abuse (e.g. “You inspire me to fall asleep”) paraphrasing simple explicitly abusive utterances (e.g. “You are boring”). For this task, we introduce a novel dataset that has been created via crowdsourcing. Special attention has been paid to the generation of appropriate negative (non-abusive) data. We report on classification experiments showing that classifiers trained on previous datasets are less capable of detecting such abuse. Best automatic results are obtained by a classifier that augments training data from our new dataset with automatically-generated GPT-3 completions. We also present a classifier that combines a few manually extracted features that exemplify the major linguistic phenomena constituting euphemistic abuse.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.1012</identifier>
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%0 Conference Proceedings
%T Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language
%A Wiegand, Michael
%A Kampfmeier, Jana
%A Eder, Elisabeth
%A Ruppenhofer, Josef
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wiegand-etal-2023-euphemistic
%X We address the task of identifying euphemistic abuse (e.g. “You inspire me to fall asleep”) paraphrasing simple explicitly abusive utterances (e.g. “You are boring”). For this task, we introduce a novel dataset that has been created via crowdsourcing. Special attention has been paid to the generation of appropriate negative (non-abusive) data. We report on classification experiments showing that classifiers trained on previous datasets are less capable of detecting such abuse. Best automatic results are obtained by a classifier that augments training data from our new dataset with automatically-generated GPT-3 completions. We also present a classifier that combines a few manually extracted features that exemplify the major linguistic phenomena constituting euphemistic abuse.
%R 10.18653/v1/2023.emnlp-main.1012
%U https://aclanthology.org/2023.emnlp-main.1012
%U https://doi.org/10.18653/v1/2023.emnlp-main.1012
%P 16280-16297

Markdown (Informal)

[Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language](https://aclanthology.org/2023.emnlp-main.1012) (Wiegand et al., EMNLP 2023)

• Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language (Wiegand et al., EMNLP 2023)

ACL

• Michael Wiegand, Jana Kampfmeier, Elisabeth Eder, and Josef Ruppenhofer. 2023. Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16280–16297, Singapore. Association for Computational Linguistics.