Transformer-based language model approaches to automated story generation
currently provide state-of-the-art results. However, they still suffer from
plot incoherence when generating narratives over time, and critically lack
basic commonsense reasoning. Furthermore, existing methods generally focus only
on single-character stories, or fail to track characters at all. To improve the
coherence of generated narratives and to expand the scope of character-centric
narrative generation, we introduce Commonsense-inference Augmented neural
StoryTelling (CAST), a framework for introducing commonsense reasoning into the
generation process with the option to model the interaction between multiple
characters. We find that our CAST method produces significantly more coherent,
on-topic, enjoyable, and fluent stories than existing models in both the
single-character and two-character settings in three storytelling domains.

论文介绍了一种名为 CAST 的基于 Transformer 的神经叙事生成框架，它使用常识推理来增强故事生成的连贯性和角色之间的交互，且在单一和多角色设置下比现有模型产生更连贯，主题相关，可享受和流畅的故事。

推断读者：以常识推理为引导的自动化故事生成

Inferring the Reader: Guiding Automated Story Generation with  Commonsense Reasoning

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.

本研究使用神经叙事方法对情感轨迹进行了建模，包括情感监督和两个情感增强模型。经过自动化和手动评估，这些模型在生成故事的情感路线方面比基线方法具有显著优势，而且不会降低故事的质量。