Hierarchical CVAE for Fine-Grained Hate Speech Classification
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.
Keywords
Cite
@article{arxiv.1809.00088,
title = {Hierarchical CVAE for Fine-Grained Hate Speech Classification},
author = {Jing Qian and Mai ElSherief and Elizabeth Belding and William Yang Wang},
journal= {arXiv preprint arXiv:1809.00088},
year = {2018}
}