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

  • Nabil Hossain ,
  • John Krumm ,
  • Lucy Vanderwende ,
  • ,
  • Henry Kautz

EMNLP 2017 |

Published by Association for Computational Linguistics

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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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Filling the Blanks for Mad Libs

This data contains our custom Mad Libs that accompany the EMNLP 2017 paper called “Filling the Blanks for Mad Libs Humor”. There are 50 Mad Libs in total. For each, we provide our original Mad Lib (with no copyright). The blanks in these Mad Libs include the original word and the hint type (e.g. animal, food, noun, adverb). We also provide words that were filled in by Mechanical Turk workers along with Mechanical Turk funniness scores for each filled-in word and for the resulting filled-in Mad Lib as a whole.

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