论文标题
使用深度学习模型用于语法错误处理的最新趋势
Recent Trends in the Use of Deep Learning Models for Grammar Error Handling
论文作者
论文摘要
语法错误处理（GEH）是自然语言处理（NLP）的重要主题。 GEH包括语法误差检测和语法误差校正。计算系统的最新进展促进了用于NLP问题（例如GEH）的深度学习模型（DL）模型。在这项调查中，我们重点介绍了GEH的两种主要DL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，培训和推论。此外，我们讨论了在管道的每个阶段提高这些模型的性能的不同技术。我们比较了不同模型的性能，并以提出的未来方向结论。
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.
