Modeling Human Mental States with an Entity-based Narrative Graph

• URL: http://arxiv.org/abs/2104.07079v1
• Date: Wed, 14 Apr 2021 19:05:19 GMT
• Title: Modeling Human Mental States with an Entity-based Narrative Graph
• Authors: I-Ta Lee, Maria Leonor Pacheco and Dan Goldwasser
• Abstract summary: This paper proposes an Entity-based Narrative Graph (ENG) to model the internal-states 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 evaluate our model on two narrative understanding tasks: predicting character mental states, and desire fulfillment, and conduct a qualitative analysis.
• Score: 31.275150336289578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding narrative text requires capturing characters' motivations, goals, and mental states. This paper proposes an Entity-based Narrative Graph (ENG) to model the internal-states 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.

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