@inproceedings{hasan-etal-2019-ur,
title = "{UR}-{FUNNY}: A Multimodal Language Dataset for Understanding Humor",
author = "Hasan, Md Kamrul and
Rahman, Wasifur and
Bagher Zadeh, AmirAli and
Zhong, Jianyuan and
Tanveer, Md Iftekhar and
Morency, Louis-Philippe and
Hoque, Mohammed (Ehsan)",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1211",
doi = "10.18653/v1/D19-1211",
pages = "2046--2056",
abstract = "Humor is a unique and creative communicative behavior often displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (visual) 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 has been understudied. 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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%0 Conference Proceedings
%T UR-FUNNY: A Multimodal Language Dataset for Understanding Humor
%A Hasan, Md Kamrul
%A Rahman, Wasifur
%A Bagher Zadeh, AmirAli
%A Zhong, Jianyuan
%A Tanveer, Md Iftekhar
%A Morency, Louis-Philippe
%A Hoque, Mohammed (Ehsan)
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F hasan-etal-2019-ur
%X Humor is a unique and creative communicative behavior often displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (visual) 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 has been understudied. 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.
%R 10.18653/v1/D19-1211
%U https://aclanthology.org/D19-1211
%U https://doi.org/10.18653/v1/D19-1211
%P 2046-2056
Markdown (Informal)
[UR-FUNNY: A Multimodal Language Dataset for Understanding Humor](https://aclanthology.org/D19-1211) (Hasan et al., EMNLP-IJCNLP 2019)
ACL
- Md Kamrul Hasan, Wasifur Rahman, AmirAli Bagher Zadeh, Jianyuan Zhong, Md Iftekhar Tanveer, Louis-Philippe Morency, and Mohammed (Ehsan) Hoque. 2019. UR-FUNNY: A Multimodal Language Dataset for Understanding Humor. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2046–2056, Hong Kong, China. Association for Computational Linguistics.