Towards Conversational Humor Analysis and Design

TL;DR

This paper explores the classification and generation of conversational humor by combining machine learning, neural models, and rule-based approaches, evaluated through human studies to improve humor understanding and creation.

Contribution

It introduces a hybrid model that merges classical rule-based methods with neural networks for punchline generation based on the Incongruity Theory.

Findings

The hybrid model outperforms purely neural or rule-based approaches.

Human evaluators preferred the hybrid model's jokes over baseline models.

The approach advances automatic humor generation in conversational AI.

Abstract

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.