@inproceedings{hossain-etal-2017-filling,
title = "Filling the Blanks (hint: plural noun) for Mad {L}ibs Humor",
author = "Hossain, Nabil and
Krumm, John and
Vanderwende, Lucy and
Horvitz, Eric and
Kautz, Henry",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1067",
doi = "10.18653/v1/D17-1067",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Filling the Blanks (hint: plural noun) for Mad Libs Humor
%A Hossain, Nabil
%A Krumm, John
%A Vanderwende, Lucy
%A Horvitz, Eric
%A Kautz, Henry
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F hossain-etal-2017-filling
%X 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.
%R 10.18653/v1/D17-1067
%U https://aclanthology.org/D17-1067
%U https://doi.org/10.18653/v1/D17-1067
%P 638-647
Markdown (Informal)
[Filling the Blanks (hint: plural noun) for Mad Libs Humor](https://aclanthology.org/D17-1067) (Hossain et al., EMNLP 2017)
ACL
- Nabil Hossain, John Krumm, Lucy Vanderwende, Eric Horvitz, and Henry Kautz. 2017. Filling the Blanks (hint: plural noun) for Mad Libs Humor. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 638–647, Copenhagen, Denmark. Association for Computational Linguistics.