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.

翻译：情感及其进化在创造令人振奋的故事方面发挥着核心作用。 在本文中,我们展示了首份关于神经故事故事故事主角情感轨迹模型的研究。 我们设计了一些方法来产生符合故事标题和主角想要的情感弧线的故事。 我们的模型包括情感监督(EmoSup)和两个情感-情感强化(EmoRL)模型。 EmoRL模型使用特别奖励,旨在通过强化学习来规范故事生成过程。 我们的自动和手工评估表明,这些模型在产生与基线方法相比,与理想情感弧线相比,在不牺牲故事质量的情况下,在产生故事生成故事时,效果要好得多。