This paper presents the first study using neural machine translation (NMT) for grammatical error correction (GEC). We propose a two-step approach to handle the rare word problem in NMT, which has been proved to be useful and effective for the GEC task. Our best NMT-based system trained on the CLC outperforms our SMT-based system when testing on the publicly available FCE test set. The same system achieves an F0.5 score of 39.90% on the CoNLL-2014 shared task test set, outperforming the state-of-the-art and demonstrating that the NMT-based GEC system generalises effectively.
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