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％。