Department of Library and Information Science, Kyonggi University, Gyeonggi-do 16227, Korea.
Sensors (Basel). 2021 Apr 10;21(8):2658. doi: 10.3390/s21082658.
Grammatical Error Correction (GEC) is the task of detecting and correcting various grammatical errors in texts. Many previous approaches to the GEC have used various mechanisms including rules, statistics, and their combinations. Recently, the performance of the GEC in English has been drastically enhanced due to the vigorous applications of deep neural networks and pretrained language models. Following the promising results of the English GEC tasks, we apply the Transformer with Copying Mechanism into the Korean GEC task by introducing novel and effective noising methods for constructing Korean GEC datasets. Our comparative experiments showed that the proposed system outperforms two commercial grammar check and other NMT-based models.
语法错误修正（GEC）是检测和修正文本中各种语法错误的任务。许多以前的 GEC 方法都使用了各种机制，包括规则、统计数据及其组合。最近，由于深度神经网络和预训练语言模型的大力应用，英语 GEC 的性能得到了极大的提高。在英语 GEC 任务取得有希望的结果之后，我们通过引入新颖有效的噪声方法来构建韩语 GEC 数据集，将带复制机制的转换器应用到韩语 GEC 任务中。我们的对比实验表明，所提出的系统优于两个商业语法检查器和其他基于 NMT 的模型。
