Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning

Understand

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.