  We treat grammatical error correction (GEC) as a classification problem in
this study, where for different types of errors, a target word is identified,
and the classifier predicts the correct word form from a set of possible
choices. We propose a novel neural network based feature representation and
classification model, trained using large text corpora without human
annotations. Specifically we use RNNs with attention to represent both the left
and right context of a target word. All feature embeddings are learned jointly
in an end-to-end fashion. Experimental results show that our novel approach
outperforms other classifier methods on the CoNLL-2014 test set (F0.5 45.05%).
Our model is simple but effective, and is suitable for industrial production.
