Detecting and classifying instances of hate in social media text has been a problem of interest in Natural Language Processing in the recent years. Our work leverages state of the art Transformer language models to identify hate speech in a multilingual setting. Capturing the intent of a post or a comment on social media involves careful evaluation of the language style, semantic content and additional pointers such as hashtags and emojis. In this paper, we look at the problem of identifying whether a Twitter post is hateful and offensive or not. We further discriminate the detected toxic content into one of the following three classes: (a) Hate Speech (HATE), (b) Offensive (OFFN) and (c) Profane (PRFN). With a pre-trained multilingual Transformer-based text encoder at the base, we are able to successfully identify and classify hate speech from multiple languages. On the provided testing corpora, we achieve Macro F1 scores of 90.29, 81.87 and 75.40 for English, German and Hindi respectively while performing hate speech detection and of 60.70, 53.28 and 49.74 during fine-grained classification. In our experiments, we show the efficacy of Perspective API features for hate speech classification and the effects of exploiting a multilingual training scheme. A feature selection study is provided to illustrate impacts of specific features upon the architecture's classification head.

中文翻译：

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利用多语言变压器进行仇恨语音检测

近年来，对社交媒体文本中的仇恨实例进行检测和分类一直是自然语言处理中关注的问题。我们的工作利用最先进的Transformer语言模型来识别多语言环境中的仇恨言论。捕捉社交媒体上的帖子或评论的意图涉及对语言风格，语义内容以及其他指针（例如主题标签和表情符号）的仔细评估。在本文中，我们着眼于确定Twitter帖子是否令人讨厌和令人反感的问题。我们进一步将检测到的有毒成分分为以下三类之一：（a）仇恨言论（HATE），（b）令人反感（OFFN）和（c）亵渎（PRFN）。在基础上使用经过预训练的基于多语言Transformer的文本编码器，我们能够成功地识别和分类来自多种语言的仇恨言论。在提供的测试语料库上，在执行仇恨语音检测时，英语，德语和北印度语的Macro F1分数分别为90.29、81.87和75.40，而在细粒度分类中，Mac F1分数分别为60.70、53.28和49.74。在我们的实验中，我们展示了Perspective API功能对仇恨语音分类的功效以及开发多语言培训方案的效果。提供了一项功能选择研究，以说明特定功能对体系结构分类头的影响。在进行细粒度分类时为74。在我们的实验中，我们展示了Perspective API功能对仇恨语音分类的功效以及开发多语言培训方案的效果。提供了一项功能选择研究，以说明特定功能对体系结构分类头的影响。在进行细粒度分类时为74。在我们的实验中，我们展示了Perspective API功能对仇恨语音分类的功效以及开发多语言培训方案的效果。提供了一项功能选择研究，以说明特定功能对体系结构分类头的影响。