Hierarchical CVAE for Fine-Grained Hate Speech Classification

Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang


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.
Anthology ID:
D18-1391
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3550–3559
Language:
URL:
https://aclanthology.org/D18-1391
DOI:
10.18653/v1/D18-1391
Bibkey:
Cite (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.
Cite (Informal):
Hierarchical CVAE for Fine-Grained Hate Speech Classification (Qian et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1391.pdf
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