  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.
