Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results

TL;DR

This paper introduces a new multimodal dataset for spontaneous humor detection, evaluates various models across modalities, and proposes a novel multimodal architecture that improves humor recognition performance.

Contribution

The paper presents the Passau-SFCH dataset for spontaneous humor, analyzes modality contributions, and introduces a new multimodal Transformer architecture for humor recognition.

Findings

Facial expressions are most effective for sentiment analysis.

Text features best model humor direction.

The proposed multimodal architecture outperforms existing methods.

Abstract

Humor is a substantial element of human social behavior, affect, and cognition. Its automatic understanding can facilitate a more naturalistic human-AI interaction. Current methods of humor detection have been exclusively based on staged data, making them inadequate for "real-world" applications. We contribute to addressing this deficiency by introducing the novel Passau-Spontaneous Football Coach Humor (Passau-SFCH) dataset, comprising about 11 hours of recordings. The Passau-SFCH dataset is annotated for the presence of humor and its dimensions (sentiment and direction) as proposed in Martin's Humor Style Questionnaire. We conduct a series of experiments employing pretrained Transformers, convolutional neural networks, and expert-designed features. The performance of each modality (text, audio, video) for spontaneous humor recognition is analyzed and their complementarity is investigated. Our findings suggest that for the automatic analysis of humor and its sentiment, facial expressions are most promising, while humor direction can be best modeled via text-based features. Further, we experiment with different multimodal approaches to humor recognition, including decision-level fusion and MulT, a multimodal Transformer approach. In this context, we propose a novel multimodal architecture that yields the best overall results. Finally, we make our code publicly available at https://www.github.com/lc0197/passau-sfch. The Passau-SFCH dataset is available upon request.