  Generating humor and quotes are very challenging problems in the field of
computational linguistics and are often tackled separately. In this paper, we
present a controlled Long Short-Term Memory (LSTM) architecture which is
trained with categorical data like jokes and quotes together by passing
category as an input along with the sequence of words. The idea is that a
single neural net will learn the structure of both jokes and quotes to generate
them on demand according to input category. Importantly, we believe the neural
net has more knowledge as it's trained on different datasets and hence will
enable it to generate more creative jokes or quotes from the mixture of
information. May the network generate a funny inspirational joke!
