Racist or Sexist Meme? Classifying Memes beyond Hateful
Racist or Sexist Meme? Classifying Memes beyond Hateful
复制标题
种族主义或性别歧视模因？
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Gareth Tyson
中科院分区：
文献类型：
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作者：
Haris Bin Zia;Ignacio Castro;Gareth Tyson
模因是文字和图像的组合，本质上通常是幽默的。但是，情况可能并不总是这样，文本和图像的某些组合可能描绘了仇恨，称为可恨的模因。这项工作提出了一个多通道管道，考虑到来自模因的视觉和文本特征，以(1)识别受保护的类别(例如种族、性别等)。以及(2)检测攻击类型(例如，蔑视、诽谤等)。我们的流程使用最先进的预先训练的视觉和文本表示法，然后是简单的Logistic回归分类器。我们在仇恨模因挑战数据集上使用了我们的流水线，并为受保护的类别和攻击类型添加了新创建的细粒度标签。我们的最佳模型实现了用于识别受保护类别的AUROC为0.96，用于检测攻击类型的AUROC为0.97。我们在https://github.com/harisbinzia/HatefulMemes发布我们的代码
Memes are the combinations of text and images that are often humorous in nature. But, that may not always be the case, and certain combinations of texts and images may depict hate, referred to as hateful memes. This work presents a multimodal pipeline that takes both visual and textual features from memes into account to (1) identify the protected category (e.g. race, sex etc.) that has been attacked; and (2) detect the type of attack (e.g. contempt, slurs etc.). Our pipeline uses state-of-the-art pre-trained visual and textual representations, followed by a simple logistic regression classifier. We employ our pipeline on the Hateful Memes Challenge dataset with additional newly created fine-grained labels for protected category and type of attack. Our best model achieves an AUROC of 0.96 for identifying the protected category, and 0.97 for detecting the type of attack. We release our code at https://github.com/harisbinzia/HatefulMemes