  Hateful Memes is a new challenge set for multimodal classification, focusing
on detecting hate speech in multimodal memes. Difficult examples are added to
the dataset to make it hard to rely on unimodal signals, which means only
multimodal models can succeed. According to Kiela,the state-of-the-art methods
perform poorly compared to humans (64.73% vs. 84.7% accuracy) on Hateful Memes.
I propose a new model that combined multimodal with rules, which achieve the
first ranking of accuracy and AUROC of 86.8% and 0.923 respectively. These
rules are extracted from training set, and focus on improving the
classification accuracy of difficult samples.
