论文笔记：Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples论文笔记：Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples
论文：Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples文章简要介绍出处：EMNLP 2020作者来自：复旦大学 Shanghai Key Laboratory of Intelligent Information Processing解决问题：GEC模型的性能依赖于语料库大小和质量的问题思路：受对抗训练启发，通过不断识别模型弱点生成对抗样本，并将生成的对抗样本添加到训练集中
