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We propose a novel data synthesis method to generate diverse error-corrected sentence pairs for improving grammatical error correction, which is based on a pair of machine translation models of different qualities (i.e., poor and good).
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Marek Rei, Mariano Felice, Zheng Yuan, and Ted Briscoe. 2017 · 2017
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Tao Ge, Furu Wei, and Ming Zhou. 2018 · 2018
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The bea-2019 shared task on grammatical error correction
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