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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.naacl-main.391.pdf
- Code
- doug919/entity_based_narrative_graph
- Data
- DesireDB