  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.
