A Corpus for Understanding and Generating Moral Stories
This work addresses the challenge of teaching morals through storytelling for AI systems, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of bridging story plots and implied morals by introducing STORAL, a dataset of Chinese and English moral stories, and proposed tasks to assess machine understanding and generation, showing difficulty with various models and improving performance using a retrieval-augmented algorithm.
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.