Meme captioning, the task of generating a sentence that describes the meaning of a meme, is both challenging and important in advancing Computational Meme Understanding (CMU). However, existing research has not explored its decomposition into subtasks or its connections to other CMU tasks. To address this gap, we introduce MemeInterpret, a meme corpus containing meme captions together with corresponding surface messages and relevant background knowledge. Strategically built upon the Facebook Hateful Memes dataset, MemeInterpret is the last piece in a set of corpora that unifies three major categories of CMU tasks for the first time. Extensive experiments on MemeInterpret and connected datasets suggest strong relationships between meme captioning, its two proposed subtasks, and the other two key categories of CMU tasks: classification and explanation. To stimulate further research on CMU, we make our dataset publicly available at https://github.com/npnkhoi/MemeInterpret.

While recent years have seen a surge of interest in the automatic processing of memes, much of the work in this area has focused on determining whether a meme contains malicious content. This paper proposes the new task of intent description generation: generating a description of the author’s intentions when creating the meme. To stimulate future work on this task, we (1) annotated a corpus of memes with the intents being perceived by the reader as well as the background knowledge needed to infer the intents and (2) established baseline performance on the intent description generation task using state-of-the-art large language models. Our results suggest the importance of background knowledge retrieval in intent description generation for memes.