  The subjective nature of humor makes computerized humor generation a
challenging task. We propose an automatic humor generation framework for
filling the blanks in Mad Libs stories, while accounting for the demographic
backgrounds of the desired audience. We collect a dataset consisting of such
stories, which are filled in and judged by carefully selected workers on Amazon
Mechanical Turk. We build upon the BERT platform to predict location-biased
word fillings in incomplete sentences, and we fine tune BERT to classify
location-specific humor in a sentence. We leverage these components to produce
YodaLib, a fully-automated Mad Libs style humor generation framework, which
selects and ranks appropriate candidate words and sentences in order to
generate a coherent and funny story tailored to certain demographics. Our
experimental results indicate that YodaLib outperforms a previous
semi-automated approach proposed for this task, while also surpassing human
annotators in both qualitative and quantitative analyses.
