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:文字错误校正(GEC)是自动发现和纠正文本错误的任务。任务不仅包括纠正语法错误,例如缺少预设位置和不匹配的主题动词协议,而且包括拼写和语义错误,例如拼写错误和字选错误。在过去十年里,这个领域取得了显著进展,其部分原因是一系列五种共同任务,这五种任务推动了基于规则的方法、统计分类、统计机器翻译和最后神经机器翻译系统的开发,这些系统代表了目前艺术的主导状态。在本调查文件中,我们将字段压缩成一篇文章,并首先概述了任务的一些语言挑战,介绍了研究人员最受欢迎的数据集(英语和其他语言的数据集),并总结了以人为错误生成为特别重点开发的各种方法和技术。我们接下来将描述许多不同的评价方法,以及有关衡量可靠性的问题,特别是主观的人类判断,然后总结最近的进展和建议,提出未来工作及未来挑战。我们希望这次调查将成为新的研究人员的全面资源,以便他们了解最新的实地动态。我们希望调查成为最新的资源。