Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarise the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgements, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.

翻译：语法错误纠正（GEC）是指自动检测并纠正文本中错误的任务。该任务不仅涵盖语法错误（如介词缺失、主谓不一致等），还包括拼写错误和语义错误（如词汇选择不当）。过去十年间，该领域取得了显著进展，部分动力源于连续五届共享任务——这些任务推动了基于规则的方法、统计分类器、统计机器翻译乃至当前主流的神经机器翻译系统的发展。本文作为综述性论文，首先梳理任务所涉及的语言学挑战，介绍研究人员可获取的（英语及其他语言的）主流数据集，并系统总结各类方法和技术，重点聚焦于人工错误生成方法。其次，我们详述了多种评估方法，以及围绕指标可靠性（尤其在主观人工评判方面）的争议。最后概述最新进展，并为未来研究方向和待解决问题提出建议。希望本综述能为初入该领域或希望跟进最新发展的研究者提供全面参考。