社交媒体中仇恨言论检测的跨语言胶囊网络

Aiqi Jiang, A. Zubiaga
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引用次数: 5

摘要

大多数仇恨言论检测研究都集中在单一语言上,通常是英语,这限制了它们对其他语言的推广。在本文中,我们研究了跨语言仇恨言论检测任务,通过将仇恨言论资源从一种语言调整到另一种语言来解决这个问题。我们提出了一种针对仇恨言论的跨语言胶囊网络学习模型,该模型结合了额外的领域特定词汇语义(CCNL-Ex)。我们的模型在来自AMI@Evalita2018和AMI@Ibereval2018的基准数据集上实现了最先进的性能,涉及三种语言:英语、西班牙语和意大利语,在所有六种语言对上都优于最先进的基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-lingual Capsule Network for Hate Speech Detection in Social Media
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.
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