摘要
全面调研2015年至2021年间基于深度学习的学习者语法纠错研究,将其分为通用型和适用型两大类型并详细分析其研究方法;介绍预训练语言模型和语料库数据的类型和作用,并对比不同的评估指标以及系统的纠错性能;对现有研究进行综合评价。未来应重点关注:1)构建适用型、个性化纠错系统。2)深度分析模型的劣势,从以下两个方面探索增强其推理能力的方法:(1)探索预训练语言模型的应用方法;(2)构建多模型混合系统。
This paper proposed a comprehensive survey of deep learning-based grammatical error correction(GEC)from 2015 to 2021.GEC research could be divided into universal and adaptive paradigms under which the basic approaches were analyzed.The types and effects of pre-trained language models,public corpus data and evaluation metrics were introduced.Furthermore,a comprehensive evaluation on major GEC systems performance was conducted.It is suggested that future research should focus on the following aspects:1)building a suitable and personalized error correction system;2)exploring methods to enhance the reasoning ability of deep analysis models from the following two aspects:(1)exploring the application methods of pre trained language models,and(2)constructing multi model hybrid systems.
作者
杨林伟
Yang Linwei(School of Foreign Langauges,Yantai University,Yantai 264005,Shandong,China)
出处
《计算机应用与软件》
北大核心
2025年第7期12-21,65,共11页
Computer Applications and Software
基金
山东省高等学校人文社科研究计划项目(J14WD07)
山东省社会科学规划研究项目(19CWZJ29)。
关键词
语法错误
学习者英语
深度学习
机器翻译
序列标注
Grammatical error
Learner English
Deep learning
Machine translation
Sequence tagging
