SICKNet: A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge

Abstract

Humor is a great linguistic tool to express feelings and enhance social bonding. Limited by the diversity of humor expressions and the differential understanding of listeners, automatic detection of humor text is still a difficult and important area in nature language processing. Current methods of humor detection mainly focus on fine-tuning of pre-trained language models, and rarely consider the degree of humor incongruity and knowledge distinction of contextual environments. To alleviate these challenges, we propose SICKNet, a novel multi-tasks learning network based on the incongruity theory of humor and commonsense knowledge. We first utilize the difference between set-up and punchline to detect the semantic incongruity of humor, and next use commonsense knowledge to detect the strength of humorous features. SICKNet achieves the start-of-the-art results on Reddit and TaivopJokes datasets, with accuracy rates of 76.27% and 73.64%, respectively. Our code is available at Github11https://github.com/xing-wei-zeng/SICKNet.