UR-FUNNY: A Multimodal Language Dataset for Understanding Humor
원문 초록
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.
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The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor Recognition
SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation
Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models
Comment-aided Video-Language Alignment via Contrastive Pre-training for Short-form Video Humor Detection
MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts
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