A Corpus for Understanding and Generating Moral Stories
Jian Guan | Ziqi Liu | Minlie Huang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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.
A Random Walk Approach to Selectional Preferences Based on Preference Ranking and Propagation
Zhenhua Tian | Hengheng Xiang | Ziqi Liu | Qinghua Zheng
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)