Neural sequence-to-sequence (seq2seq) approaches have proven to be successful
in grammatical error correction (GEC). Based on the seq2seq framework, we
propose a novel fluency boost learning and inference mechanism. Fluency
boosting learning generates diverse error-corrected sentence pairs during
training, enabling the error correction model to learn how to improve a
sentence's fluency from more instances, while fluency boosting inference allows
the model to correct a sentence incrementally with multiple inference steps.
Combining fluency boost learning and inference with convolutional seq2seq
models, our approach achieves the state-of-the-art performance: 75.72 (F_{0.5})
on CoNLL-2014 10 annotation dataset and 62.42 (GLEU) on JFLEG test set
respectively, becoming the first GEC system that reaches human-level
performance (72.58 for CoNLL and 62.37 for JFLEG) on both of the benchmarks.

该论文提出了一种新颖的流利性提升学习和推理机制，结合卷积 seq2seq 模型，成功将神经序列到序列的方法应用于语法错误纠正 (GEC)，在 CoNLL-2014 10 注释数据集上获得了 75.72（F_{0.5}）的最好表现，在 JFLEG 测试集上获得了 62.42（GLEU)）的最好表现，并成为首个在这两项基准测试中都达到人类水平性能（分别为 CoNLL-2014 72.58 和 JFLEG 62.37）的 GEC 系统。