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        <title>Don’t be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks</title>
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    <abstract>Offensive language detection is an important task for filtering out abusive expressions and improving online user experiences. However, malicious users often attempt to avoid filtering systems through the involvement of textual noises. In this paper, we propose these evasions as user-intended adversarial attacks that insert special symbols or leverage the distinctive features of the Korean language. Furthermore, we introduce simple yet effective pooling strategies in a layer-wise manner to defend against the proposed attacks, focusing on the preceding layers not just the last layer to capture both offensiveness and token embeddings. We demonstrate that these pooling strategies are more robust to performance degradation even when the attack rate is increased, without directly training of such patterns. Notably, we found that models pre-trained on clean texts could achieve a comparable performance in detecting attacked offensive language, to models pre-trained on noisy texts by employing these pooling strategies.</abstract>
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%0 Conference Proceedings
%T Don’t be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks
%A Yu, Seunguk
%A Choi, Juhwan
%A Kim, YoungBin
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F yu-etal-2024-dont
%X Offensive language detection is an important task for filtering out abusive expressions and improving online user experiences. However, malicious users often attempt to avoid filtering systems through the involvement of textual noises. In this paper, we propose these evasions as user-intended adversarial attacks that insert special symbols or leverage the distinctive features of the Korean language. Furthermore, we introduce simple yet effective pooling strategies in a layer-wise manner to defend against the proposed attacks, focusing on the preceding layers not just the last layer to capture both offensiveness and token embeddings. We demonstrate that these pooling strategies are more robust to performance degradation even when the attack rate is increased, without directly training of such patterns. Notably, we found that models pre-trained on clean texts could achieve a comparable performance in detecting attacked offensive language, to models pre-trained on noisy texts by employing these pooling strategies.
%U https://aclanthology.org/2024.findings-naacl.219
%P 3456-3467

Markdown (Informal)

[Don’t be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks](https://aclanthology.org/2024.findings-naacl.219) (Yu et al., Findings 2024)

• Don’t be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks (Yu et al., Findings 2024)

ACL

• Seunguk Yu, Juhwan Choi, and YoungBin Kim. 2024. Don’t be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3456–3467, Mexico City, Mexico. Association for Computational Linguistics.