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.
@article{arxiv.2201.12438,
title = {Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey},
author = {Prajjwal Bhargava and Vincent Ng},
journal= {arXiv preprint arXiv:2201.12438},
year = {2022}
}