Modeling Protagonist Emotions for Emotion-Aware Storytelling

Faeze Brahman, Snigdha Chaturvedi


Abstract
Emotions and their evolution play a central role in creating a captivating story. In this paper, we present the first study on modeling the emotional trajectory of the protagonist in neural storytelling. We design methods that generate stories that adhere to given story titles and desired emotion arcs for the protagonist. Our models include Emotion Supervision (EmoSup) and two Emotion-Reinforced (EmoRL) models. The EmoRL models use special rewards designed to regularize the story generation process through reinforcement learning. Our automatic and manual evaluations demonstrate that these models are significantly better at generating stories that follow the desired emotion arcs compared to baseline methods, without sacrificing story quality.
Anthology ID:
2020.emnlp-main.426
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5277–5294
Language:
URL:
https://aclanthology.org/2020.emnlp-main.426
DOI:
10.18653/v1/2020.emnlp-main.426
Bibkey:
Cite (ACL):
Faeze Brahman and Snigdha Chaturvedi. 2020. Modeling Protagonist Emotions for Emotion-Aware Storytelling. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5277–5294, Online. Association for Computational Linguistics.
Cite (Informal):
Modeling Protagonist Emotions for Emotion-Aware Storytelling (Brahman & Chaturvedi, EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2020.emnlp-main.426.pdf
Video:
 https://slideslive.com/38939253
Code
 fabrahman/Emo-Aware-Storytelling
Data
ROCStories