What Do You MEME? Generating Explanations for Visual Semantic Role Labelling in Memes

Memes are powerful means for effective communication on social media. Their effortless amalgamation of viral visuals and compelling messages can have far-reaching implications with proper marketing. Previous research memes has primarily focused characterizing their affective spectrum detecting whether the meme's message insinuates any intended harm, such as hate, offense, racism, etc. However, often use abstraction, which be elusive. Here, we introduce a novel task - EXCLAIM, generating explanations visual semantic role labeling in memes. To this end, curate ExHVV, dataset that offers natural language connotative roles three types entities heroes, villains, victims, encompassing 4,680 present 3K We also benchmark ExHVV several strong unimodal multimodal baselines. Moreover, posit LUMEN, multimodal, multi-task learning framework endeavors to address EXCLAIM optimally by jointly predict correct correspondingly generate suitable explanations. LUMEN distinctly outperforms best baseline across 18 standard generation evaluation metrics. Our systematic analyses demonstrate characteristic cues required adjudicating helpful