Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey
It provides a comprehensive overview for researchers in natural language processing, but it is incremental as it synthesizes existing work rather than introducing new methods.
This survey examines the use of pre-trained language models for commonsense knowledge reasoning and generation tasks, highlighting their strengths and weaknesses as revealed by recent benchmarks.
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.