论文标题
螃蟹：班级表示社交媒体中的仇恨言论识别
CRAB: Class Representation Attentive BERT for Hate Speech Identification in Social Media
论文作者
论文摘要
近年来，社交媒体平台主持了仇恨言论和令人难以置信的内容的爆炸。对有效自动仇恨言论检测模型的迫切需求吸引了公司和研究人员的巨大投资。社交媒体帖子通常很短，即使是一个令牌，他们的语义也可能会大大改变。因此，对于此任务，学习上下文感知的输入表示形式，并将输入嵌入和类表示之间的相关得分视为附加信号是至关重要的。为了满足这些需求，本文介绍了Crab（班级表示BERT），这是一种用于在社交媒体中检测仇恨言论的神经模型。该模型受益于两个语义表示：（i）可训练的令牌和句子类表示，以及（ii）来自最先进的bert编码器的上下文化输入嵌入。为了调查螃蟹的有效性，我们在Twitter数据上训练模型，并将其与强基础进行比较。我们的结果表明，与最先进的基线相比，螃蟹可实现1.89％的相对改善的宏观平均F1。这项研究的结果为在社交媒体中对自动虐待行为检测的未来研究打开了机会
In recent years, social media platforms have hosted an explosion of hate speech and objectionable content. The urgent need for effective automatic hate speech detection models have drawn remarkable investment from companies and researchers. Social media posts are generally short and their semantics could drastically be altered by even a single token. Thus, it is crucial for this task to learn context-aware input representations, and consider relevancy scores between input embeddings and class representations as an additional signal. To accommodate these needs, this paper introduces CRAB (Class Representation Attentive BERT), a neural model for detecting hate speech in social media. The model benefits from two semantic representations: (i) trainable token-wise and sentence-wise class representations, and (ii) contextualized input embeddings from state-of-the-art BERT encoder. To investigate effectiveness of CRAB, we train our model on Twitter data and compare it against strong baselines. Our results show that CRAB achieves 1.89% relative improved Macro-averaged F1 over state-of-the-art baseline. The results of this research open an opportunity for the future research on automated abusive behavior detection in social media
