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We treat grammatical error correction (GEC) as a classification problem inthis study, where for different types of errors, a target word is identified,and the classifier predicts the correct word form from a set of possiblechoices. We propose a novel neural network based feature representation andclassification model, trained using large text corpora without humanannotations. Specifically we use RNNs with attention to represent both the leftand right context of a target word. All feature embeddings are learned jointlyin an end-to-end fashion. Experimental results show that our novel approachoutperforms 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.

A Simple but Effective Classification Model for Grammatical Error Correction