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        <title>Detect All Abuse! Toward Universal Abusive Language Detection Models</title>
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        <namePart type="given">Kunze</namePart>
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    <name type="personal">
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        <namePart type="family">Han</namePart>
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    <name type="personal">
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    <name type="personal">
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            <namePart type="given">Chengqing</namePart>
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    <abstract>Online abusive language detection (ALD) has become a societal issue of increasing importance in recent years. Several previous works in online ALD focused on solving a single abusive language problem in a single domain, like Twitter, and have not been successfully transferable to the general ALD task or domain. In this paper, we introduce a new generic ALD framework, MACAS, which is capable of addressing several types of ALD tasks across different domains. Our generic framework covers multi-aspect abusive language embeddings that represent the target and content aspects of abusive language and applies a textual graph embedding that analyses the user’s linguistic behaviour. Then, we propose and use the cross-attention gate flow mechanism to embrace multiple aspects of abusive language. Quantitative and qualitative evaluation results show that our ALD algorithm rivals or exceeds the six state-of-the-art ALD algorithms across seven ALD datasets covering multiple aspects of abusive language and different online community domains.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.560</identifier>
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%0 Conference Proceedings
%T Detect All Abuse! Toward Universal Abusive Language Detection Models
%A Wang, Kunze
%A Lu, Dong
%A Han, Caren
%A Long, Siqu
%A Poon, Josiah
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F wang-etal-2020-detect
%X Online abusive language detection (ALD) has become a societal issue of increasing importance in recent years. Several previous works in online ALD focused on solving a single abusive language problem in a single domain, like Twitter, and have not been successfully transferable to the general ALD task or domain. In this paper, we introduce a new generic ALD framework, MACAS, which is capable of addressing several types of ALD tasks across different domains. Our generic framework covers multi-aspect abusive language embeddings that represent the target and content aspects of abusive language and applies a textual graph embedding that analyses the user’s linguistic behaviour. Then, we propose and use the cross-attention gate flow mechanism to embrace multiple aspects of abusive language. Quantitative and qualitative evaluation results show that our ALD algorithm rivals or exceeds the six state-of-the-art ALD algorithms across seven ALD datasets covering multiple aspects of abusive language and different online community domains.
%R 10.18653/v1/2020.coling-main.560
%U https://aclanthology.org/2020.coling-main.560
%U https://doi.org/10.18653/v1/2020.coling-main.560
%P 6366-6376

Markdown (Informal)

[Detect All Abuse! Toward Universal Abusive Language Detection Models](https://aclanthology.org/2020.coling-main.560) (Wang et al., COLING 2020)

• Detect All Abuse! Toward Universal Abusive Language Detection Models (Wang et al., COLING 2020)

ACL

• Kunze Wang, Dong Lu, Caren Han, Siqu Long, and Josiah Poon. 2020. Detect All Abuse! Toward Universal Abusive Language Detection Models. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6366–6376, Barcelona, Spain (Online). International Committee on Computational Linguistics.