Commonsense Knowledge Reasoning and Generation with Pre-Trained Language Models: A Survey
Abstract
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.
Cite
Text
Bhargava and Ng. "Commonsense Knowledge Reasoning and Generation with Pre-Trained Language Models: A Survey." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21496Markdown
[Bhargava and Ng. "Commonsense Knowledge Reasoning and Generation with Pre-Trained Language Models: A Survey." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/bhargava2022aaai-commonsense/) doi:10.1609/AAAI.V36I11.21496BibTeX
@inproceedings{bhargava2022aaai-commonsense,
title = {{Commonsense Knowledge Reasoning and Generation with Pre-Trained Language Models: A Survey}},
author = {Bhargava, Prajjwal and Ng, Vincent},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {12317-12325},
doi = {10.1609/AAAI.V36I11.21496},
url = {https://mlanthology.org/aaai/2022/bhargava2022aaai-commonsense/}
}