SICKNet: A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge

Penglong Huang; Xingwei Zeng; Jinta Weng; Ying Gao; Heyan Huang; Maobin Tang

doi:10.1109/ICTAI56018.2022.00049

SICKNet: A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge

Penglong Huang, Xingwei Zeng, Jinta Weng^*, Ying Gao, Heyan Huang, Maobin Tang^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Humor is a great linguistic tool to express feelings and enhance social bonding. Limited by the diversity of humor expressions and the differential understanding of listeners, automatic detection of humor text is still a difficult and important area in nature language processing. Current methods of humor detection mainly focus on fine-tuning of pre-trained language models, and rarely consider the degree of humor incongruity and knowledge distinction of contextual environments. To alleviate these challenges, we propose SICKNet, a novel multi-tasks learning network based on the incongruity theory of humor and commonsense knowledge. We first utilize the difference between set-up and punchline to detect the semantic incongruity of humor, and next use commonsense knowledge to detect the strength of humorous features. SICKNet achieves the start-of-the-art results on Reddit and TaivopJokes datasets, with accuracy rates of 76.27% and 73.64%, respectively. Our code is available at Github11https://github.com/xing-wei-zeng/SICKNet.

Original language	English
Title of host publication	Proceedings - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022
Editors	Marek Reformat, Du Zhang, Nikolaos G. Bourbakis
Publisher	IEEE Computer Society
Pages	288-296
Number of pages	9
ISBN (Electronic)	9798350397444
DOIs	https://doi.org/10.1109/ICTAI56018.2022.00049
Publication status	Published - 2022
Externally published	Yes
Event	34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022 - Virtual, Online, China Duration: 31 Oct 2022 → 2 Nov 2022

Publication series

Name	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume	2022-October
ISSN (Print)	1082-3409

Conference

Conference	34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022
Country/Territory	China
City	Virtual, Online
Period	31/10/22 → 2/11/22

Keywords

Common-sense Knowledge
Humor Detection
Multi-task learning
Natural Language Processing
Semantic Incongruity

Access to Document

10.1109/ICTAI56018.2022.00049

Cite this

Huang, P., Zeng, X., Weng, J., Gao, Y., Huang, H., & Tang, M. (2022). SICKNet: A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge. In M. Reformat, D. Zhang, & N. G. Bourbakis (Eds.), Proceedings - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022 (pp. 288-296). (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/ICTAI56018.2022.00049

Huang, Penglong ; Zeng, Xingwei ; Weng, Jinta et al. / SICKNet : A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge. Proceedings - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022. editor / Marek Reformat ; Du Zhang ; Nikolaos G. Bourbakis. IEEE Computer Society, 2022. pp. 288-296 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI).

@inproceedings{1fabf51629464ab08eb09f6c372d8d21,

title = "SICKNet: A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge",

abstract = "Humor is a great linguistic tool to express feelings and enhance social bonding. Limited by the diversity of humor expressions and the differential understanding of listeners, automatic detection of humor text is still a difficult and important area in nature language processing. Current methods of humor detection mainly focus on fine-tuning of pre-trained language models, and rarely consider the degree of humor incongruity and knowledge distinction of contextual environments. To alleviate these challenges, we propose SICKNet, a novel multi-tasks learning network based on the incongruity theory of humor and commonsense knowledge. We first utilize the difference between set-up and punchline to detect the semantic incongruity of humor, and next use commonsense knowledge to detect the strength of humorous features. SICKNet achieves the start-of-the-art results on Reddit and TaivopJokes datasets, with accuracy rates of 76.27% and 73.64%, respectively. Our code is available at Github11https://github.com/xing-wei-zeng/SICKNet.",

keywords = "Common-sense Knowledge, Humor Detection, Multi-task learning, Natural Language Processing, Semantic Incongruity",

author = "Penglong Huang and Xingwei Zeng and Jinta Weng and Ying Gao and Heyan Huang and Maobin Tang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022 ; Conference date: 31-10-2022 Through 02-11-2022",

year = "2022",

doi = "10.1109/ICTAI56018.2022.00049",

language = "English",

series = "Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI",

publisher = "IEEE Computer Society",

pages = "288--296",

editor = "Marek Reformat and Du Zhang and Bourbakis, {Nikolaos G.}",

booktitle = "Proceedings - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022",

address = "United States",

}

Huang, P, Zeng, X, Weng, J, Gao, Y, Huang, H & Tang, M 2022, SICKNet: A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge. in M Reformat, D Zhang & NG Bourbakis (eds), Proceedings - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, vol. 2022-October, IEEE Computer Society, pp. 288-296, 34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022, Virtual, Online, China, 31/10/22. https://doi.org/10.1109/ICTAI56018.2022.00049

SICKNet: A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge. / Huang, Penglong; Zeng, Xingwei; Weng, Jinta et al.
Proceedings - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022. ed. / Marek Reformat; Du Zhang; Nikolaos G. Bourbakis. IEEE Computer Society, 2022. p. 288-296 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - SICKNet

T2 - 34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022

AU - Huang, Penglong

AU - Zeng, Xingwei

AU - Weng, Jinta

AU - Gao, Ying

AU - Huang, Heyan

AU - Tang, Maobin

PY - 2022

Y1 - 2022

N2 - Humor is a great linguistic tool to express feelings and enhance social bonding. Limited by the diversity of humor expressions and the differential understanding of listeners, automatic detection of humor text is still a difficult and important area in nature language processing. Current methods of humor detection mainly focus on fine-tuning of pre-trained language models, and rarely consider the degree of humor incongruity and knowledge distinction of contextual environments. To alleviate these challenges, we propose SICKNet, a novel multi-tasks learning network based on the incongruity theory of humor and commonsense knowledge. We first utilize the difference between set-up and punchline to detect the semantic incongruity of humor, and next use commonsense knowledge to detect the strength of humorous features. SICKNet achieves the start-of-the-art results on Reddit and TaivopJokes datasets, with accuracy rates of 76.27% and 73.64%, respectively. Our code is available at Github11https://github.com/xing-wei-zeng/SICKNet.

AB - Humor is a great linguistic tool to express feelings and enhance social bonding. Limited by the diversity of humor expressions and the differential understanding of listeners, automatic detection of humor text is still a difficult and important area in nature language processing. Current methods of humor detection mainly focus on fine-tuning of pre-trained language models, and rarely consider the degree of humor incongruity and knowledge distinction of contextual environments. To alleviate these challenges, we propose SICKNet, a novel multi-tasks learning network based on the incongruity theory of humor and commonsense knowledge. We first utilize the difference between set-up and punchline to detect the semantic incongruity of humor, and next use commonsense knowledge to detect the strength of humorous features. SICKNet achieves the start-of-the-art results on Reddit and TaivopJokes datasets, with accuracy rates of 76.27% and 73.64%, respectively. Our code is available at Github11https://github.com/xing-wei-zeng/SICKNet.

KW - Common-sense Knowledge

KW - Humor Detection

KW - Multi-task learning

KW - Natural Language Processing

KW - Semantic Incongruity

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U2 - 10.1109/ICTAI56018.2022.00049

DO - 10.1109/ICTAI56018.2022.00049

M3 - Conference contribution

AN - SCOPUS:85156170043

T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

SP - 288

EP - 296

BT - Proceedings - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022

A2 - Reformat, Marek

A2 - Zhang, Du

A2 - Bourbakis, Nikolaos G.

PB - IEEE Computer Society

Y2 - 31 October 2022 through 2 November 2022

ER -

Huang P, Zeng X, Weng J, Gao Y, Huang H, Tang M. SICKNet: A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge. In Reformat M, Zhang D, Bourbakis NG, editors, Proceedings - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022. IEEE Computer Society. 2022. p. 288-296. (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI). doi: 10.1109/ICTAI56018.2022.00049

SICKNet: A Humor Detection Network Integrating Semantic Incongruity and Commonsense Knowledge

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