论文标题
常识性知识推理和使用预训练的语言模型的产生：调查
Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey
论文作者
论文摘要
尽管常识性知识获取和推理传统上一直是知识代表和推理社区中的核心研究主题，但近年来，人们对自然语言处理社区的兴趣激增，在开发预训练的模型并测试其解决各种新设计的常识性知识推理和生成任务的能力。本文介绍了这些任务的调查，讨论了这些任务所揭示的常识性推理和产生的最先进的预培训模型的优势和缺点，并反映了未来的研究方向。
While commonsense knowledge acquisition and reasoning has traditionally been a core research topic in the knowledge representation and reasoning community, recent years have seen a surge of interest in the natural language processing community in developing pre-trained models and testing their ability to address a variety of newly designed commonsense knowledge reasoning and generation tasks. This paper presents a survey of these tasks, discusses the strengths and weaknesses of state-of-the-art pre-trained models for commonsense reasoning and generation as revealed by these tasks, and reflects on future research directions.
