Story Completion with Explicit Modeling of Commonsense Knowledge

Mingda Zhang, Keren Ye, Rebecca Hwa, Adriana Kovashka

Abstract: Growing up with bedtime tales, even children could easily tell how a story should develop; but selecting a coherent and reasonable ending for a story is still not easy for machines. To successfully choose an ending requires not only detailed analysis of the context, but also applying commonsense reasoning and basic knowledge. Previous work has shown that language models trained on very large corpora could capture common sense in an implicit and hard-to-interpret way. We explore another direction and present a novel method that explicitly incorporates commonsense knowledge from a structured dataset, and demonstrate the potential for improving story completion.