Teaching morals is one of the most important purposes of storytelling. An
essential ability for understanding and writing moral stories is bridging story
plots and implied morals. Its challenges mainly lie in: (1) grasping knowledge
about abstract concepts in morals, (2) capturing inter-event discourse
relations in stories, and (3) aligning value preferences of stories and morals
concerning good or bad behavior. In this paper, we propose two understanding
tasks and two generation tasks to assess these abilities of machines. We
present STORAL, a new dataset of Chinese and English human-written moral
stories. We show the difficulty of the proposed tasks by testing various models
with automatic and manual evaluation on STORAL. Furthermore, we present a
retrieval-augmented algorithm that effectively exploits related concepts or
events in training sets as additional guidance to improve performance on these
tasks.

本文提出了两项理解任务和两项生成任务，以评估机器的道德故事理解和写作能力，并提供了一个新的汉英双语人工写作的道德故事数据集 STORAL，使用检索增强算法对模型进行训练，并在自动和手动评估中测试了不同模型的性能。