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.
翻译:最近,经过培训的模型(PTMs)的出现使自然语言处理(NLP)进入了一个新时代。在本次调查中,我们为NLP提供了对PTM的全面审查。我们首先简要地介绍了语言代表学习及其研究进展。然后,我们从四个角度对现有的PTM进行系统分类。接下来,我们描述如何使PTM的知识适应下游任务。最后,我们为今后的研究概述了PTM的一些潜在方向。这次调查旨在成为理解、使用和开发用于各种NLP任务的PTM的实用指南。