Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning
Xiangyu Peng, Siyan Li, Sarah Wiegreffe, Mark Riedl · 2022
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 singlecharacter 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), 1 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.