Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10% M$^2$ on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2% on the CoNLL-2014 benchmark and by 4% on JFLEG.

该论文提出了一套适用于大多数语法错误修正（GEC）环境的模型无关方法，包括添加源端噪音，领域自适应技术，GEC特定训练目标，单语数据的迁移学习以及集成独立训练的GEC模型和语言模型。这些方法的综合效果导致比现有最优神经GEC模型更好的表现，表现优于CoNLL-2014基准测试中以前最好的神经GEC系统超过10％M2，以及JFLEG测试集中的5.9％。

将神经语法错误纠正作为低资源机器翻译任务进行处理