Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning

Transformer-based language model approaches to automated story generation currently provide state-of-the-art results. However, they still suffer from plot incoherence when generating narratives over time, and critically lack basic commonsense reasoning. Furthermore, existing methods generally focus only on single-character stories, or fail to track characters at all. To improve the coherence of generated narratives and to expand the scope of character-centric narrative generation, we introduce Commonsense-inference Augmented neural StoryTelling (CAST), a framework for introducing commonsense reasoning into the generation process while modeling the interaction between multiple characters. We find that our CAST method produces significantly more coherent and on-topic two-character stories, outperforming baselines in dimensions including plot plausibility and staying on topic. We also show how the CAST method can be used to further train language models that generate more coherent stories and reduce computation cost.