Psychology-guided Controllable Story Generation

Yuqiang Xie, Yue Hu, Yunpeng Li, Guanqun Bi, Luxi Xing, Wei Peng


Abstract
Controllable story generation is a challenging task in the field of NLP, which has attracted increasing research interest in recent years. However, most existing works generate a whole story conditioned on the appointed keywords or emotions, ignoring the psychological changes of the protagonist. Inspired by psychology theories, we introduce global psychological state chains, which include the needs and emotions of the protagonists, to help a story generation system create more controllable and well-planned stories. In this paper, we propose a Psychology-guided Controllable Story Generation System (PICS) to generate stories that adhere to the given leading context and desired psychological state chains for the protagonist. Specifically, psychological state trackers are employed to memorize the protagonist’s local psychological states to capture their inner temporal relationships. In addition, psychological state planners are adopted to gain the protagonist’s global psychological states for story planning. Eventually, a psychology controller is designed to integrate the local and global psychological states into the story context representation for composing psychology-guided stories. Automatic and manual evaluations demonstrate that PICS outperforms baselines, and each part of PICS shows effectiveness for writing stories with more consistent psychological changes.
Anthology ID:
2022.coling-1.564
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6480–6492
Language:
URL:
https://aclanthology.org/2022.coling-1.564
DOI:
Bibkey:
Cite (ACL):
Yuqiang Xie, Yue Hu, Yunpeng Li, Guanqun Bi, Luxi Xing, and Wei Peng. 2022. Psychology-guided Controllable Story Generation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6480–6492, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Psychology-guided Controllable Story Generation (Xie et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.564.pdf
Data
Story Commonsense