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Recent studies have proposed models that yielded promising performance forthe hateful meme classification task. Nevertheless, these proposed models donot generate interpretable explanations that uncover the underlying meaning andsupport the classification output. A major reason for the lack of explainablehateful meme methods is the absence of a hateful meme dataset that containsground truth explanations for benchmarking or training. Intuitively, havingsuch explanations can educate and assist content moderators in interpreting andremoving flagged hateful memes. This paper address this research gap byintroducing Hateful meme with Reasons Dataset (HatReD), which is a newmultimodal hateful meme dataset annotated with the underlying hatefulcontextual reasons. We also define a new conditional generation task that aimsto automatically generate underlying reasons to explain hateful memes andestablish the baseline performance of state-of-the-art pre-trained languagemodels on this task. We further demonstrate the usefulness of HatReD byanalyzing the challenges of the new conditional generation task in explainingmemes in seen and unseen domains. The dataset and benchmark models are madeavailable here: https://github.com/Social-AI-Studio/HatRed

Decoding the Underlying Meaning of Multimodal Hateful Memes