Modeling Protagonist Emotions for Emotion-Aware Storytelling

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 (Emo-Sup) 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. Title (input): Raw burger Emotion arc (input): joy → anger → sadness Story (output): Tom went to a burger place with his friends. He ordered a burger. When he got it , he noticed that it was raw. Tom yelled at the waiter for it being raw. He was really disappointed.