论文标题
在线多语言仇恨言论识别系统
An Online Multilingual Hate speech Recognition System
论文作者
论文摘要
在过去的二十年中，互联网和社交媒体使用的指数增加改变了人类的互动。这导致了许多积极的结果，但与此同时，它带来了风险和危害。尽管在线有害内容的数量（例如仇恨言论）无法由人类管理，但对学术界的兴趣调查了仇恨言论检测手段的兴趣增加了。在这项研究中，我们通过将它们组合成单个均匀的数据集并将其分为三个类，分别为虐待，仇恨或两种类别，分析了六个公开可用的数据集。我们创建一个基线模型，并使用各种优化技术提高模型性能得分。达到竞争性能得分后，我们创建了一个工具，该工具在近乎实际的时间内标识和分数有效的指标，并使用与反馈相同的指标来重新培训我们的模型。我们证明了我们在两个Langauges（英语和印地语）上多语言模型的竞争性能，从而与大多数单语模型相当或卓越的性能。
The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.
