@InProceedings{hossain-EtAl:2017:EMNLP2017,
  author    = {Hossain, Nabil  and  Krumm, John  and  Vanderwende, Lucy  and  Horvitz, Eric  and  Kautz, Henry},
  title     = {Filling the Blanks (hint: plural noun) for Mad Libs Humor},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {638--647},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1067}
}