  Well-defined jokes can be divided neatly into a setup and a punchline. While
most works on humor today talk about a joke as a whole, the idea of generating
punchlines to a setup has applications in conversational humor, where funny
remarks usually occur with a non-funny context. Thus, this paper is based
around two core concepts: Classification and the Generation of a punchline from
a particular setup based on the Incongruity Theory. We first implement a
feature-based machine learning model to classify humor. For humor generation,
we use a neural model, and then merge the classical rule-based approaches with
the neural approach to create a hybrid model. The idea behind being: combining
insights gained from other tasks with the setup-punchline model and thus
applying it to existing text generation approaches. We then use and compare our
model with human written jokes with the help of human evaluators in a
double-blind study.
