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2017 EMNLP EMNLP 2017

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

Abstract

AbstractComputerized 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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Natural Language Processing
📈 Trend Setter — Natural Language Generation
🧭 Keyword Pioneer — machine learning classifier
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Weakly Supervised Learning Natural Language Processing > Applications > Text Generation Machine Learning > Learning Types > Classification Artificial Intelligence > Core AI > Natural Language Generation

Keywords

text classification natural language generation supervised learning machine learning classifier humor generation machine learned classifier fill-in-the-blank game ground truth datum amazon mechanical turk
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