Humour Style

Humour style research aims to computationally understand and classify the diverse ways humour is expressed, recognizing its varied psychological and social impacts. Current research focuses on developing computational models, including neural networks and transformer-based architectures, to analyze textual and multimodal data (incorporating visual and acoustic cues) for humour detection and style classification, often leveraging techniques from related fields like sarcasm recognition. This work is significant for advancing our understanding of human communication and emotion, with potential applications in areas such as mental health assessment, improving human-computer interaction, and creating more sophisticated AI systems capable of generating and understanding humour.