Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy with four perspectives. Next, we describe how to adapt the knowledge of PTMs to the downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.
@article{arxiv.2003.08271,
title = {Pre-trained Models for Natural Language Processing: A Survey},
author = {Xipeng Qiu and Tianxiang Sun and Yige Xu and Yunfan Shao and Ning Dai and Xuanjing Huang},
journal= {arXiv preprint arXiv:2003.08271},
year = {2021}
}
Comments
Invited Review of Science China Technological Sciences