United States

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:

NAACL 2021

Modeling Human Mental States with an Entity-based Narrative Graph

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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https://d3smihljt9218e.cloudfront.net/transcript/14153/subtitle/dd1a6522864b83b8d32d0aa4a2aba1bc.vtt

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Modeling Human Mental States with an Entity-based Narrative Graph

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Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters

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