Filling the Blanks (hint: plural noun) for Mad Libs Humor

Nabil Hossain, John Krumm, Lucy Vanderwende, Eric Horvitz, Henry Kautz


Abstract
Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib, which is a popular fill-in-the-blank game. The algorithm is based on a machine learned classifier that determines whether a potential fill-in word is funny in the context of the Mad Lib story. We use Amazon Mechanical Turk to create ground truth data and to judge humor for our classifier to mimic, and we make this data freely available. Our testing shows that Libitum successfully aids humans in filling in Mad Libs that are usually judged funnier than those filled in by humans with no computerized help. We go on to analyze why some words are better than others at making a Mad Lib funny.
Anthology ID:
D17-1067
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
638–647
Language:
URL:
https://www.aclweb.org/anthology/D17-1067
DOI:
10.18653/v1/D17-1067
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1067.pdf
Video:
 https://vimeo.com/238228773