  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.
