Harshith Kayam


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Guiding Neural Story Generation with Reader Models
Xiangyu Peng | Kaige Xie | Amal Alabdulkarim | Harshith Kayam | Samihan Dani | Mark Riedl
Findings of the Association for Computational Linguistics: EMNLP 2022

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