@inproceedings{35afe90863b44c45a8843f48edb2db4c,
title = "TVSHOWGUESS: Character Comprehension in Stories as Speaker Guessing",
abstract = "We propose a new task for assessing machines' ability to understand 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 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.",
author = "Yisi Sang and Xiangyang Mou and Mo Yu and Shunyu Yao and Jing Li and Jeffrey Stanton",
note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022 ; Conference date: 10-07-2022 Through 15-07-2022",
year = "2022",
language = "English (US)",
series = "NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "4267--4287",
booktitle = "NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics",
address = "United States",
}