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Abstract
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.
Original language | English |
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Title of host publication | Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP |
Publisher | Association for Computational Linguistics |
Pages | 217-225 |
Number of pages | 9 |
Publication status | Published - 2009 |
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