In this paper we develop a story generator that leverages knowledge inherent in corpora without requiring extensive manual involvement. A key feature in our approach is the reliance on a story planner which we acquire automatically by recording events, their participants, and their precedence relationships in a training corpus. Contrary to previous work our system does not follow a generate-and-rank architecture. Instead, we employ evolutionary search techniques to explore the space of possible stories which we argue are well suited to the story generation task. Experiments on generating simple children’s stories show that our system outperforms previous data-driven approaches.
|Title of host publication||Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics|
|Publisher||Association for Computational Linguistics|
|Number of pages||11|
|Publication status||Published - 2010|