Guiding Neural Story Generation with Reader Models
Xiangyu Peng, Kaige Xie, Amal Alabdulkarim, Harshith Kayam, Samihan Dani, Mark Riedl
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-topictoward a specific ending when generating narratives with neural language models. In this paper, we introduce Story generation with ReaderModels (StoRM), a framework in which areader model is used to reason about the storyshould progress. A reader model infers whata human reader believes about the concepts,entities, and relations about the fictional storyworld. We show how an explicit reader modelrepresented as a knowledge graph affords the storycoherence and provides controllability in theform of achieving a given story world stategoal. Experiments show that our model produces significantly more coherent and on-topicstories, outperforming baselines in dimensionsincluding plot plausibility and staying on topic
BibTeX
@inproceedings{peng-etal-2022-guiding,
title = "Guiding Neural Story Generation with Reader Models",
author = "Peng, Xiangyu and
Xie, Kaige and
Alabdulkarim, Amal and
Kayam, Harshith and
Dani, Samihan and
Riedl, Mark",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.526/",
doi = "10.18653/v1/2022.findings-emnlp.526",
pages = "7087--7111"
}