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Teaching morals is one of the most important purposes of storytelling. Anessential ability for understanding and writing moral stories is bridging storyplots and implied morals. Its challenges mainly lie in: (1) grasping knowledgeabout abstract concepts in morals, (2) capturing inter-event discourserelations in stories, and (3) aligning value preferences of stories and moralsconcerning good or bad behavior. In this paper, we propose two understandingtasks and two generation tasks to assess these abilities of machines. Wepresent STORAL, a new dataset of Chinese and English human-written moralstories. We show the difficulty of the proposed tasks by testing various modelswith automatic and manual evaluation on STORAL. Furthermore, we present aretrieval-augmented algorithm that effectively exploits related concepts orevents in training sets as additional guidance to improve performance on thesetasks.

A Corpus for Understanding and Generating Moral Stories