Cross-lingual Capsule Network for Hate Speech Detection in Social Media
This addresses the problem of limited generalizability in hate speech detection for non-English languages, though it is incremental as it builds on existing methods.
The paper tackles cross-lingual hate speech detection by adapting resources from one language to another, proposing a capsule network model with lexical semantics that achieves state-of-the-art performance on benchmark datasets in English, Spanish, and Italian, outperforming baselines on all six language pairs.
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.