Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning

• URL: http://arxiv.org/abs/2105.01311v3
• Date: Fri, 17 Nov 2023 23:37:23 GMT
• Title: Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning
• Authors: Xiangyu Peng, Siyan Li, Sarah Wiegreffe, Mark Riedl
• Abstract summary: We introduce Commonsense-inference Augmented neural StoryTelling (CAST), a framework for introducing commonsense reasoning into the generation process. 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.
• Score: 12.264880519328353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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