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Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged words from the source sentence to the target sentence. Since the GEC suffers from not having enough labeled training data to achieve high accuracy. We pre-train the copy-augmented architecture with a denoising auto-encoder using the unlabeled One Billion Benchmark and makedoi:10.18653/v1/n19-1014 dblp:conf/naacl/ZhaoWSJL19 fatcat:gnstwmpncfemhbhi4gqpynd2ni