NAACL 2021long3 citations
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.
BibTeX
@inproceedings{lee-etal-2021-modeling,
title = "Modeling Human Mental States with an Entity-based Narrative Graph",
author = "Lee, I-Ta and
Pacheco, Maria Leonor and
Goldwasser, Dan",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.391/",
doi = "10.18653/v1/2021.naacl-main.391",
pages = "4916--4926"
}