  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.
