使用深度学习模型处理语法错误的最新趋势
Recent Trends in the Use of Deep Learning Models for Grammar Error Handling
摘要：
语法错误处理是自然语言处理中的一个重要课题。GEH包括语法错误检测和语法错误纠正。计算系统的最新进展促进了深度学习（DL）模型在GEH等NLP问题中的使用。在这篇综述中，我们重点介绍了GEH的两种主要的深度学习方法：神经机器翻译模型和编辑器模型。我们描述了这些模型管道的三个主要阶段：数据准备，训练和推理。此外，我们讨论了不同的技术，以提高这些模型在管道的每个阶段的性能。我们比较了不同模型的性能，并得出结论，提出了未来的发展方向。
英文摘要：
Grammar error handling (GEH) is an important topic in natural language processing (NLP). GEH includes both grammar error detection and grammar error correction. Recent advances in computation systems have promoted the use of deep learning (DL) models for NLP problems such as GEH. In this survey we focus on two main DL approaches for GEH: neural machine translation models and editor models. We describe the three main stages of the pipeline for these models: data preparation, training, and inference. Additionally, we discuss different techniques to improve the performance of these models at each stage of the pipeline. We compare the performance of different models and conclude with proposed future directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
