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        <title>Learning to Decipher Hate Symbols</title>
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        <namePart type="given">Jing</namePart>
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            <namePart type="given">Christy</namePart>
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    <abstract>Existing computational models to understand hate speech typically frame the problem as a simple classification task, bypassing the understanding of hate symbols (e.g., 14 words, kigy) and their secret connotations. In this paper, we propose a novel task of deciphering hate symbols. To do this, we leveraged the Urban Dictionary and collected a new, symbol-rich Twitter corpus of hate speech. We investigate neural network latent context models for deciphering hate symbols. More specifically, we study Sequence-to-Sequence models and show how they are able to crack the ciphers based on context. Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.</abstract>
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%0 Conference Proceedings
%T Learning to Decipher Hate Symbols
%A Qian, Jing
%A ElSherief, Mai
%A Belding, Elizabeth
%A Wang, William Yang
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F qian-etal-2019-learning
%X Existing computational models to understand hate speech typically frame the problem as a simple classification task, bypassing the understanding of hate symbols (e.g., 14 words, kigy) and their secret connotations. In this paper, we propose a novel task of deciphering hate symbols. To do this, we leveraged the Urban Dictionary and collected a new, symbol-rich Twitter corpus of hate speech. We investigate neural network latent context models for deciphering hate symbols. More specifically, we study Sequence-to-Sequence models and show how they are able to crack the ciphers based on context. Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.
%R 10.18653/v1/N19-1305
%U https://aclanthology.org/N19-1305
%U https://doi.org/10.18653/v1/N19-1305
%P 3006-3015

Markdown (Informal)

[Learning to Decipher Hate Symbols](https://aclanthology.org/N19-1305) (Qian et al., NAACL 2019)

• Learning to Decipher Hate Symbols (Qian et al., NAACL 2019)

ACL

• Jing Qian, Mai ElSherief, Elizabeth Belding, and William Yang Wang. 2019. Learning to Decipher Hate Symbols. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3006–3015, Minneapolis, Minnesota. Association for Computational Linguistics.