论文标题
理解和产生道德故事的语料库
A Corpus for Understanding and Generating Moral Stories
论文作者
论文摘要
教学道德是讲故事的最重要目的之一。理解和撰写道德故事的基本能力是弥合故事情节和隐含的道德。它的挑战主要在于：（1）掌握有关道德中抽象概念的知识，（2）捕获故事中事实间的话语关系，以及（3）对故事和道德的价值偏好和关于好是坏行为的价值偏好。在本文中，我们提出了两项理解任务和两个一代任务，以评估机器的这些能力。我们提出了Storal，这是一个新的中文和英语人工编写的道德故事的数据集。我们通过在存储上使用自动和手动评估来测试各种模型来显示提出的任务的困难。此外，我们提出了一种检索算法，该算法有效利用培训集中的相关概念或事件，作为提高这些任务绩效的附加指南。
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.
