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UR-FUNNY: A Multimodal Language Dataset for Understanding Humor (1904.06618v1)

Published 14 Apr 2019 in cs.LG, cs.CL, and stat.ML

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.

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Authors (8)
  1. Md Kamrul Hasan (71 papers)
  2. Wasifur Rahman (8 papers)
  3. Amir Zadeh (36 papers)
  4. Jianyuan Zhong (13 papers)
  5. Louis-Philippe Morency (123 papers)
  6. Mohammed (3 papers)
  7. Hoque (3 papers)
  8. Md Iftekhar Tanveer (4 papers)
Citations (159)

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