An implemented model of punning riddles
Kim Binsted (Department of Artificial Intelligence, University of, Edinburgh), Graeme Ritchie (Department of Artificial Intelligence, University, of Edinburgh)
TL;DR
This paper presents JAPE, a program that models punning riddles using schemata and templates, successfully generating jokes from lexical entries, with potential for quality improvements.
Contribution
It introduces a computational model for punning riddles that combines schemata and templates, advancing automatic joke generation.
Findings
JAPE generates recognizable jokes from lexical entries.
Some generated jokes are not very good, indicating room for improvement.
Post-production heuristics could enhance joke quality.
Abstract
In this paper, we discuss a model of simple question-answer punning, implemented in a program, JAPE, which generates riddles from humour-independent lexical entries. The model uses two main types of structure: schemata, which determine the relationships between key words in a joke, and templates, which produce the surface form of the joke. JAPE succeeds in generating pieces of text that are recognizably jokes, but some of them are not very good jokes. We mention some potential improvements and extensions, including post-production heuristics for ordering the jokes according to quality.
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Taxonomy
TopicsHumor Studies and Applications · Comics and Graphic Narratives · Language, Metaphor, and Cognition