Cross-lingual Capsule Network for Hate Speech Detection in Social Media

10Citations
Citations of this article
26Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Most hate speech detection research focuses on a single language, generally English, which limits their generalisability to other languages. In this paper we investigate the cross-lingual hate speech detection task, tackling the problem by adapting the hate speech resources from one language to another. We propose a cross-lingual capsule network learning model coupled with extra domain-specific lexical semantics for hate speech (CCNL-Ex). Our model achieves state-of-The-Art performance on benchmark datasets from AMI@Evalita2018 and AMI@Ibereval2018 involving three languages: English, Spanish and Italian, outperforming state-of-The-Art baselines on all six language pairs.

Cite

CITATION STYLE

APA

Jiang, A., & Zubiaga, A. (2021). Cross-lingual Capsule Network for Hate Speech Detection in Social Media. In HT 2021 - Proceedings of the 32nd ACM Conference on Hypertext and Social Media (pp. 217–223). Association for Computing Machinery, Inc. https://doi.org/10.1145/3465336.3475102

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free