@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://preview.aclanthology.org/landing_page/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."
}