Towards Conversational Humor Analysis and Design

• URL: http://arxiv.org/abs/2103.00536v1
• Date: Sun, 28 Feb 2021 15:22:57 GMT
• Title: Towards Conversational Humor Analysis and Design
• Authors: Tanishq Chaudhary, Mayank Goel, Radhika Mamidi
• Abstract summary: This paper is based around two core concepts: Classification and the Generation of a punchline from a particular setup based on the Incongruity Theory. 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. We then use and compare our model with human written jokes with the help of human evaluators in a double-blind study.
• Score: 17.43766386622031
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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