Learning to Tell Tales: A Data-driven Approach to Story Generation

Computational story telling has sparked great interest in artificial intelligence, partly because of its relevance to educational and gaming applications. Traditionally, story generators rely on a large repository of background knowledge containing information about the story plot and its characters. This information is detailed and usually hand crafted. In this paper we propose a data-driven approach for generating short children's stories that does not require extensive manual involvement. We create an end-to-end system that realizes the various components of the generation pipeline stochastically. Our system follows a generate-and-and-rank approach where the space of multiple candidate stories is pruned by considering whether they are plausible, interesting, and coherent.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Neil McIntyre, Mirella Lapata
• Keywords: ACL 2009, Computational Linguistics, Computational Story, Extensive Manual Involvement, Multiple Candidate Stories