  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.
