Modeling Human Mental States with an Entity-based Narrative Graph
I-Ta Lee, María Leonor Pacheco, Dan L. Goldwasser · 2021
Understanding narrative text requires capturing characters' motivations, goals, and mental states.This paper proposes an Entity-based Narrative Graph (ENG) to model the internalstates of characters in a story.We explicitly model entities, their interactions and the context in which they appear, and learn rich representations for them.We experiment with different task-adaptive pre-training objectives, in-domain training, and symbolic inference to capture dependencies between different decisions in the output space.We evaluate our model on two narrative understanding tasks: predicting character mental states, and desire fulfillment, and conduct a qualitative analysis.