Narrative Theory for Computational Narrative Understanding

Andrew Piper, Richard Jean So, David Bamman


Abstract
Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection. While this work has brought an important empirical lens for examining narrative, it is by and large divorced from the large body of theoretical work on narrative within the humanities, social and cognitive sciences. In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications.
Anthology ID:
2021.emnlp-main.26
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
298–311
Language:
URL:
https://aclanthology.org/2021.emnlp-main.26
DOI:
10.18653/v1/2021.emnlp-main.26
Bibkey:
Cite (ACL):
Andrew Piper, Richard Jean So, and David Bamman. 2021. Narrative Theory for Computational Narrative Understanding. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 298–311, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Narrative Theory for Computational Narrative Understanding (Piper et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.26.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.26.mp4