TL;DR
This paper introduces models for generating stories with controlled emotional trajectories of protagonists, enhancing storytelling by aligning story content with specified emotion arcs using reinforcement learning.
Contribution
It presents the first neural storytelling methods that explicitly model and control protagonist emotions throughout the story.
Findings
Models outperform baselines in following desired emotion arcs
Emotion-reinforced models maintain story quality
Automatic and manual evaluations confirm effectiveness
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.
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