Hate speech detection is a critical problem in social media platforms, being often accused for enabling the spread of hatred and igniting physical violence. Hate speech detection requires overwhelming resources including high-performance computing for online posts and tweets monitoring as well as thousands of human experts for daily screening of suspected posts or tweets. Recently, Deep Learning (DL)-based solutions have been proposed for automatic detection of hate speech, using modest-sized training datasets of few thousands of hate speech sequences. While these methods perform well on the specific datasets, their ability to detect new hate speech sequences is limited and has not been investigated. Being a data-driven approach, it is well known that DL surpasses other methods whenever a scale-up in train dataset size and diversity is achieved. Therefore, we first present a dataset of 1 million realistic hate and non-hate sequences, produced by a deep generative language model. We further utilize the generated dataset to train a well-studied DL-based hate speech detector, and demonstrate consistent and significant performance improvements across five public hate speech datasets. Therefore, the proposed solution enables high sensitivity detection of a very large variety of hate speech sequences, paving the way to a fully automatic solution.

中文翻译：

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通过深度生成建模实现仇恨语音检测

仇恨言论检测是社交媒体平台中的一个关键问题，经常被指责为传播仇恨和引发身体暴力。仇恨言论检测需要大量资源，包括用于在线帖子和推文监控的高性能计算，以及用于日常筛选可疑帖子或推文的数千名人类专家。最近，已经提出了基于深度学习 (DL) 的解决方案来自动检测仇恨言论，使用包含数千个仇恨言论序列的中等规模的训练数据集。虽然这些方法在特定数据集上表现良好，但它们检测新仇恨言论序列的能力有限，尚未进行调查。作为一种数据驱动的方法，众所周知，只要实现训练数据集大小和多样性的扩大，DL 就会超越其他方法。因此，我们首先展示了一个由深度生成语言模型生成的包含 100 万个真实仇恨和非仇恨序列的数据集。我们进一步利用生成的数据集来训练一个经过充分研究的基于 DL 的仇恨言论检测器，并在五个公共仇恨言论数据集上展示了一致且显着的性能改进。因此，所提出的解决方案能够对种类繁多的仇恨言论序列进行高灵敏度检测，为全自动解决方案铺平道路。