Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. However, it is challenging to maintain coherence and stay on-topic toward a specific ending when generating narratives with neural language models. In this paper, we introduce Story generation with Reader Models (StoRM), a framework in which a reader model is used to reason about the story should progress. A reader model infers what a human reader believes about the concepts, entities, and relations about the fictional story world. We show how an explicit reader model represented as a knowledge graph affords story coherence and provides controllability in the form of achieving a given story world state goal. Experiments show that our model produces significantly more coherent and on-topic stories, outperforming baselines in dimensions including plot plausibility and staying on topic.
@article{arxiv.2112.08596,
title = {Guiding Neural Story Generation with Reader Models},
author = {Xiangyu Peng and Kaige Xie and Amal Alabdulkarim and Harshith Kayam and Samihan Dani and Mark O. Riedl},
journal= {arXiv preprint arXiv:2112.08596},
year = {2022}
}