论文标题
使用多模式深度学习方法在模因中检测仇恨言论：赢得仇恨模因挑战的解决方案
Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge
论文作者
论文摘要
互联网上的模因通常是无害的，有时是有趣的。但是，通过使用两者的某些类型的图像，文本或组合，看似无害的模因成为一种多模式的仇恨言论 - 一个可恨的模因。仇恨模因挑战是一项首要竞争，重点是在多模式模因中检测仇恨言论，它提出了一个新的数据集，其中包含10,000多个多模式内容的新示例。我们利用Visualbert（将是视觉和语言的Bert）进行了多模式训练，并应用了合奏学习。我们的方法在挑战测试集中获得了0.811 AUROC，精度为0.765，在仇恨模因挑战中的3173名参与者中排名第三。
Memes on the Internet are often harmless and sometimes amusing. However, by using certain types of images, text, or combinations of both, the seemingly harmless meme becomes a multimodal type of hate speech -- a hateful meme. The Hateful Memes Challenge is a first-of-its-kind competition which focuses on detecting hate speech in multimodal memes and it proposes a new data set containing 10,000+ new examples of multimodal content. We utilize VisualBERT -- which meant to be the BERT of vision and language -- that was trained multimodally on images and captions and apply Ensemble Learning. Our approach achieves 0.811 AUROC with an accuracy of 0.765 on the challenge test set and placed third out of 3,173 participants in the Hateful Memes Challenge.
