标题： 有损感知的课程学习用于中文语法错误修正 显示英文标题

标题： Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction

摘要： 中文语法错误纠正（CGEC）旨在检测和纠正输入中文句子中的错误。 最近，预训练语言模型（PLMS）被用于提高性能。 然而，当前的方法忽略了不同实例之间的纠正难度差异，并同等对待这些样本，增加了模型学习的难度。 为了解决这个问题，我们提出了一种多粒度课程学习（CL）框架。 具体来说，我们首先计算这些样本的纠正难度，并按批次从易到难地输入到模型中。 然后 采用实例级CL来帮助模型通过调节损失函数自动朝着适当的方向优化。 大量实验结果和对各种数据集的全面分析证明了我们方法的有效性。 显示英文摘要

摘要： Chinese grammatical error correction (CGEC) aims to detect and correct errors in the input Chinese sentences. Recently, Pre-trained Language Models (PLMS) have been employed to improve the performance. However, current approaches ignore that correction difficulty varies across different instances and treat these samples equally, enhancing the challenge of model learning. To address this problem, we propose a multi-granularity Curriculum Learning (CL) framework. Specifically, we first calculate the correction difficulty of these samples and feed them into the model from easy to hard batch by batch. Then Instance-Level CL is employed to help the model optimize in the appropriate direction automatically by regulating the loss function. Extensive experimental results and comprehensive analyses of various datasets prove the effectiveness of our method.