# UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

## TL;DR

This paper introduces UR-FUNNY, a comprehensive multimodal dataset capturing humor expressed through text, gestures, and prosody, aiming to advance multimodal humor understanding in NLP.

## Contribution

It provides the first diverse multimodal humor dataset and a framework for multimodal humor detection, facilitating research in this understudied area.

## Key findings

- UR-FUNNY enables multimodal humor analysis.
- The dataset supports development of humor detection models.
- It promotes understanding of multimodal language in social interactions.

## Abstract

Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research.