The massive spread of hate speech, hateful content targeted at specific subpopulations, is a problem of critical social importance. Automated methods for hate speech detection typically employ state-of-the-art deep learning (DL)-based text classifiers-very large pre-trained neural language models of over 100 million parameters, adapting these models to the task of hate speech detection using relevant labeled datasets. Unfortunately, there are only numerous labeled datasets of limited size that are available for this purpose. We make several contributions with high potential for advancing this state of affairs. We present HyperNetworks for hate speech detection, a special class of DL networks whose weights are regulated by a small-scale auxiliary network. These architectures operate at character-level, as opposed to word-level, and are several magnitudes of order smaller compared to the popular DL classifiers. We further show that training hate detection classifiers using large amounts of automatically generated examples in a procedure named as it data augmentation is beneficial in general, yet this practice especially boosts the performance of the proposed HyperNetworks. In fact, we achieve performance that is comparable or better than state-of-the-art language models, which are pre-trained and orders of magnitude larger, using this approach, as evaluated using five public hate speech datasets.

翻译：仇恨言论的大规模传播、针对特定亚群群的仇恨内容的大规模传播,是一个至关重要的社会问题。仇恨言论的自动检测方法通常采用最先进的深层次学习(DL)文本分类(DL)基础的文本分类(DL)特殊类别,其重量由小型辅助网络调节。这些结构在字符级别上运行,而不是在字级上运行,与流行的DL分类系统相比,其秩序规模小于几级。我们进一步表明,培训仇恨检测分类人员使用大量自动生成的、以数据增强命名的程序中的大量实例,在总体上是有益的,但这种做法特别能提升拟议的超文本网络的性能。事实上,我们使用比公共演讲前五种程度更强或更强的版本,我们使用这种更具有可比性的、更强的、更强度的语音模式来评估。