TVShowGuess: Character Comprehension in Stories as Speaker Guessing

Yisi Sang, Xiangyang Mou, Mo Yu, Shunyu Yao, Jing Li, Jeffrey Stanton


Abstract
We propose a new task for assessing machines’ skills of understanding fictional characters in narrative stories. The task, TVShowGuess, builds on the scripts of TV series and takes the form of guessing the anonymous main characters based on the backgrounds of the scenes and the dialogues. Our human study supports that this form of task covers comprehension of multiple types of character persona, including understanding characters’ personalities, facts and memories of personal experience, which are well aligned with the psychological and literary theories about the theory of mind (ToM) of human beings on understanding fictional characters during reading. We further propose new model architectures to support the contextualized encoding of long scene texts. Experiments show that our proposed approaches significantly outperform baselines, yet still largely lag behind the (nearly perfect) human performance.Our work serves as a first step toward the goal of narrative character comprehension.
Anthology ID:
2022.naacl-main.317
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4267–4287
Language:
URL:
https://aclanthology.org/2022.naacl-main.317
DOI:
10.18653/v1/2022.naacl-main.317
Bibkey:
Cite (ACL):
Yisi Sang, Xiangyang Mou, Mo Yu, Shunyu Yao, Jing Li, and Jeffrey Stanton. 2022. TVShowGuess: Character Comprehension in Stories as Speaker Guessing. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4267–4287, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
TVShowGuess: Character Comprehension in Stories as Speaker Guessing (Sang et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.317.pdf
Code
 yisisang/tvshowguess
Data
NarrativeQA