  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 with the option to model the interaction between multiple
characters. We find that our CAST method produces significantly more coherent,
on-topic, enjoyable and fluent stories than existing models in both the
single-character and two-character settings in three storytelling domains.
