UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

被引:0
|
作者
Hasan, Md Kamrul [1 ]
Rahman, Wasifur [1 ]
Zadeh, Amir [2 ]
Zhong, Jianyuan [1 ]
Tanveer, Md Iftekhar [1 ,3 ]
Morency, Louis-Philippe [2 ]
Hoque, Mohammed [1 ]
机构
[1] Univ Rochester, Dept Comp Sci, Rochester, NY 14627 USA
[2] CMU, SCS, Language Technol Inst, Pittsburgh, PA USA
[3] Comcast Appl AI Res, Washington, DC USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
SENTIMENT ANALYSIS; LAUGHTER;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:2046 / 2056
页数:11
相关论文
共 50 条
  • [31] Frame-shifting humor in simulation-based language understanding
    Bergen, B
    Coulson, S
    IEEE INTELLIGENT SYSTEMS, 2006, 21 (02) : 59 - 62
  • [32] CMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French
    Zadeh, Amir
    Cao, Yan Sheng
    Hessner, Simon
    Liang, Paul Pu
    Poria, Soujanya
    Morency, Louis-Philippe
    PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP), 2020, : 1801 - 1812
  • [33] Benchmarking Large Language Models on CFLUE - A Chinese Financial Language Understanding Evaluation Dataset
    Zhu, Jie
    Li, Junhui
    Wen, Yalong
    Guo, Lifan
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: ACL 2024, 2024, : 5673 - 5693
  • [34] MultiModal Language Modelling on Knowledge Graphs for Deep Video Understanding
    Anand, Vishal
    Ramesh, Raksha
    Jin, Boshen
    Wang, Ziyin
    Lei, Xiaoxiao
    Lin, Ching-Yung
    PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2021, 2021, : 4868 - 4872
  • [35] Improving Vision and Language Concepts Understanding with Multimodal Counterfactual Samples
    Lai, Chengen
    Song, Shengli
    Yan, Sitong
    Hu, Guangneng
    COMPUTER VISION - ECCV 2024, PT LXIX, 2025, 15127 : 174 - 191
  • [36] Multimodal Analysis for Deep Video Understanding with Video Language Transformer
    Zhang, Beibei
    Fang, Yaqun
    Ren, Tongwei
    Wu, Gangshan
    PROCEEDINGS OF THE 30TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2022, 2022, : 7165 - 7169
  • [37] Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene Understanding of Healthcare Facilities
    Mohan, Rohit
    Arce, Jose
    Mokhtar, Sassan
    Cattaneo, Daniele
    Valada, Abhinav
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2024, 9 (08): : 7094 - 7101
  • [38] Popular Hooks: A Multimodal Dataset of Musical Hooks for Music Understanding and Generation<bold> </bold>
    Wu, Xinda
    Wang, Jiaming
    Yu, Jiaxing
    Zhang, Tieyao
    Zhang, Kejun
    2024 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO WORKSHOPS, ICMEW 2024, 2024,
  • [39] MECCANO: A multimodal egocentric dataset for humans behavior understanding in the industrial-like domain
    Ragusa, Francesco
    Furnari, Antonino
    Farinella, Giovanni Maria
    COMPUTER VISION AND IMAGE UNDERSTANDING, 2023, 235
  • [40] Humor in multimodal language use: Students' Response to a dialogic, social-networking online assignment
    Song, Kwangok
    Williams, Kyle M.
    Schallert, Diane L.
    Pruitt, Alina Adonyi
    LINGUISTICS AND EDUCATION, 2021, 63