Let's All Laugh Together: A Novel Multitask Framework for Humor Detection in Internet Memes

Gitanjali Kumari, Dibyanayan Bandyopadhyay, Asif Ekbal, Santanu Pal, Arindam Chatterjee, Vinutha B. N.

Abstract: Recognizing humor in meme data is a challenging task in natural language processing (NLP) and computer vision (CV) due to the complexity and variability of humor. With the explosive growth of Internet memes on social media platforms such as Facebook, Twitter, and Instagram, this task has become more important. However, there have been few studies that investigate humor recognition from memes, particularly in languages other than English. In this work, we hypothesize that humor is closely related to the valence and arousal dimensions of sentiment. We make the first attempt to release a new meme dataset for humor recognition in Hindi and propose a multitask deep learning framework to simultaneously solve three problems: humor recognition (the primary task) and valence and arousal classification (the two secondary tasks) for Internet memes. Empirical results on the Hindi meme dataset demonstrate the efficacy of our multitask learning approach over traditional pretrained models such as BERT and VGG19. The complete resources and codes will be made available for further research after acceptance of the manuscript.