Guiding Neural Story Generation with Reader Models
- URL: http://arxiv.org/abs/2112.08596v1
- Date: Thu, 16 Dec 2021 03:44:01 GMT
- Title: Guiding Neural Story Generation with Reader Models
- Authors: Xiangyu Peng, Kaige Xie, Amal Alabdulkarim, Harshith Kayam, Samihan
Dani, Mark O. Riedl
- Abstract summary: We introduce Story generation with Reader Models (StoRM), a framework in which a reader model is used to reason about the story should progress.
Experiments show that our model produces significantly more coherent and on-topic stories, outperforming baselines in dimensions including plot plausibility and staying on topic.
- Score: 5.935317028008691
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. Our system also
outperforms outline-guided story generation baselines in composing given
concepts without ordering.
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