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        <title>Hierarchical CVAE for Fine-Grained Hate Speech Classification</title>
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        <namePart type="given">Jing</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>Existing work on automated hate speech detection typically focuses on binary classification or on differentiating among a small set of categories. In this paper, we propose a novel method on a fine-grained hate speech classification task, which focuses on differentiating among 40 hate groups of 13 different hate group categories. We first explore the Conditional Variational Autoencoder (CVAE) as a discriminative model and then extend it to a hierarchical architecture to utilize the additional hate category information for more accurate prediction. Experimentally, we show that incorporating the hate category information for training can significantly improve the classification performance and our proposed model outperforms commonly-used discriminative models.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1391</identifier>
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%0 Conference Proceedings
%T Hierarchical CVAE for Fine-Grained Hate Speech Classification
%A Qian, Jing
%A ElSherief, Mai
%A Belding, Elizabeth
%A Wang, William Yang
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F qian-etal-2018-hierarchical
%X Existing work on automated hate speech detection typically focuses on binary classification or on differentiating among a small set of categories. In this paper, we propose a novel method on a fine-grained hate speech classification task, which focuses on differentiating among 40 hate groups of 13 different hate group categories. We first explore the Conditional Variational Autoencoder (CVAE) as a discriminative model and then extend it to a hierarchical architecture to utilize the additional hate category information for more accurate prediction. Experimentally, we show that incorporating the hate category information for training can significantly improve the classification performance and our proposed model outperforms commonly-used discriminative models.
%R 10.18653/v1/D18-1391
%U https://aclanthology.org/D18-1391
%U https://doi.org/10.18653/v1/D18-1391
%P 3550-3559

Markdown (Informal)

[Hierarchical CVAE for Fine-Grained Hate Speech Classification](https://aclanthology.org/D18-1391) (Qian et al., EMNLP 2018)

• Hierarchical CVAE for Fine-Grained Hate Speech Classification (Qian et al., EMNLP 2018)

ACL

• Jing Qian, Mai ElSherief, Elizabeth Belding, and William Yang Wang. 2018. Hierarchical CVAE for Fine-Grained Hate Speech Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3550–3559, Brussels, Belgium. Association for Computational Linguistics.