“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
Faeze Brahman, Meng Huang, Oyvind Tafjord, Chao Zhao, Mrinmaya Sachan, Snigdha Chaturvedi
Abstract
When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU – a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
BibTeX
@inproceedings{brahman-etal-2021-characters-tell,
title = "{\textquotedblleft}Let Your Characters Tell Their Story{\textquotedblright}: A Dataset for Character-Centric Narrative Understanding",
author = "Brahman, Faeze and
Huang, Meng and
Tafjord, Oyvind and
Zhao, Chao and
Sachan, Mrinmaya and
Chaturvedi, Snigdha",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.150/",
doi = "10.18653/v1/2021.findings-emnlp.150",
pages = "1734--1752"
}