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.

本文调查了预先训练模型在常识推理和生成任务上的表现，探讨了最新研究的优劣势，并提出未来的研究方向。

预训练语言模型在常识知识推理和生成中的应用：综述

Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey

Commonsense knowledge acquisition is a key problem for artificial
intelligence. Conventional methods of acquiring commonsense knowledge generally
require laborious and costly human annotations, which are not feasible on a
large scale. In this paper, we explore a practical way of mining commonsense
knowledge from linguistic graphs, with the goal of transferring cheap knowledge
obtained with linguistic patterns into expensive commonsense knowledge. The
result is a conversion of ASER [Zhang et al., 2020], a large-scale selectional
preference knowledge resource, into TransOMCS, of the same representation as
ConceptNet [Liu and Singh, 2004] but two orders of magnitude larger.
Experimental results demonstrate the transferability of linguistic knowledge to
commonsense knowledge and the effectiveness of the proposed approach in terms
of quantity, novelty, and quality. TransOMCS is publicly available at:
this https URL

本文探讨了从语言图中挖掘常识知识的实用方法，将由语言模式获得的便宜知识转化为昂贵的常识知识，转换为与 ConceptNet 相同的表示方式的 TransOMCS 资源，并证明了语言知识对常识知识的可转移性和提出的方法在数量、新颖性和质量方面的有效性。