Cross-lingual Capsule Network for Hate Speech Detection in Social Media
Aiqi Jiang, Arkaitz Zubiaga
Hypertext. 2021.
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.
@inproceedings{jiang2021cross,
title={Cross-lingual Capsule Network for Hate Speech Detection in Social Media},
author={Jiang, Aiqi and Zubiaga, Arkaitz},
booktitle={Proceedings of the 32nd ACM Conference on Hypertext and Social Media},
pages={217--223},
year={2021}
}
title={Cross-lingual Capsule Network for Hate Speech Detection in Social Media},
author={Jiang, Aiqi and Zubiaga, Arkaitz},
booktitle={Proceedings of the 32nd ACM Conference on Hypertext and Social Media},
pages={217--223},
year={2021}
}