  Hateful content detection is one of the areas where deep learning can and
should make a significant difference. The Hateful Memes Challenge from Facebook
helps fulfill such potential by challenging the contestants to detect hateful
speech in multi-modal memes using deep learning algorithms. In this paper, we
utilize multi-modal, pre-trained models VilBERT and Visual BERT. We improved
models' performance by adding training datasets generated from data
augmentation. Enlarging the training data set helped us get a more than 2%
boost in terms of AUROC with the Visual BERT model. Our approach achieved
0.7439 AUROC along with an accuracy of 0.7037 on the challenge's test set,
which revealed remarkable progress.
