Grammatical error correction (GEC) is the task of detecting and correcting grammatical errors in texts written by second language learners. The statistical machine translation (SMT) approach to GEC, in which sentences written by second language learners are translated to grammatically correct sentences, has achieved state-of-the-art accuracy. However, the SMT approach is unable to utilize global context. In this paper, we propose a novel approach to improve the accuracy of GEC, by exploiting the n-best hypotheses generated by an SMT approach. Specifically, we build a classifier to score the edits in the n-best hypotheses. The classifier can be used to select appropriate edits or re-rank the n-best hypotheses. We apply these methods to a state-of-the-art GEC system that uses the SMT approach. Our experiments show that our methods achieve statistically significant improvements in accuracy over the best published results on a benchmark test dataset on GEC.

本文提出了一种利用SMT方法生成的n种翻译假设来提高GEC准确率的新方法，即利用分类器评分来选择适当的编辑或对n种翻译假设进行重新排序，并将这些方法应用于使用SMT方法的最新GEC系统中，实验结果表明，我们的方法在GEC基准测试数据集上的准确性比已发表的最佳结果有显着提高。

利用N-Best假设改进SMT法进行语法纠错