TVSHOWGUESS: Character Comprehension in Stories as Speaker Guessing

Document Type

Conference Proceeding

Publication Date

1-1-2022

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.

Identifier

85136118118 (Scopus)

ISBN

[9781955917711]

Publication Title

Naacl 2022 2022 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies Proceedings of the Conference

External Full Text Location

https://doi.org/10.18653/v1/2022.naacl-main.317

First Page

4267

Last Page

4287

Grant

CNS–1948457

Fund Ref

National Science Foundation

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