Modeling Human Mental States with an Entity-based Narrative Graph

I-Ta Lee, Maria Leonor Pacheco, Dan Goldwasser


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.
Anthology ID:
2021.naacl-main.391
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4916–4926
Language:
URL:
https://aclanthology.org/2021.naacl-main.391
DOI:
10.18653/v1/2021.naacl-main.391
Bibkey:
Cite (ACL):
I-Ta Lee, Maria Leonor Pacheco, and Dan Goldwasser. 2021. Modeling Human Mental States with an Entity-based Narrative Graph. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4916–4926, Online. Association for Computational Linguistics.
Cite (Informal):
Modeling Human Mental States with an Entity-based Narrative Graph (Lee et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.391.pdf
Video:
 https://aclanthology.org/2021.naacl-main.391.mp4
Code
 doug919/entity_based_narrative_graph
Data
DesireDB