@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",
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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<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.</abstract>
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%0 Conference Proceedings
%T Modeling Human Mental States with an Entity-based Narrative Graph
%A Lee, I-Ta
%A Pacheco, Maria Leonor
%A Goldwasser, Dan
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F lee-etal-2021-modeling
%X 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.
%R 10.18653/v1/2021.naacl-main.391
%U https://aclanthology.org/2021.naacl-main.391
%U https://doi.org/10.18653/v1/2021.naacl-main.391
%P 4916-4926
Markdown (Informal)
[Modeling Human Mental States with an Entity-based Narrative Graph](https://aclanthology.org/2021.naacl-main.391) (Lee et al., NAACL 2021)
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.