Humor Recognition Using Deep Learning

Peng-Yu Chen, Von-Wun Soo


Abstract
Humor is an essential but most fascinating element in personal communication. How to build computational models to discover the structures of humor, recognize humor and even generate humor remains a challenge and there have been yet few attempts on it. In this paper, we construct and collect four datasets with distinct joke types in both English and Chinese and conduct learning experiments on humor recognition. We implement a Convolutional Neural Network (CNN) with extensive filter size, number and Highway Networks to increase the depth of networks. Results show that our model outperforms in recognition of different types of humor with benchmarks collected in both English and Chinese languages on accuracy, precision, and recall in comparison to previous works.
Anthology ID:
N18-2018
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
113–117
Language:
URL:
https://aclanthology.org/N18-2018
DOI:
10.18653/v1/N18-2018
Bibkey:
Cite (ACL):
Peng-Yu Chen and Von-Wun Soo. 2018. Humor Recognition Using Deep Learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 113–117, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Humor Recognition Using Deep Learning (Chen & Soo, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-2018.pdf