  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). The poor translation model resembles the ESL (English as a second
language) learner and tends to generate translations of low quality in terms of
fluency and grammatical correctness, while the good translation model generally
generates fluent and grammatically correct translations. We build the poor and
good translation model with phrase-based statistical machine translation model
with decreased language model weight and neural machine translation model
respectively. By taking the pair of their translations of the same sentences in
a bridge language as error-corrected sentence pairs, we can construct unlimited
pseudo parallel data. Our approach is capable of generating diverse
fluency-improving patterns without being limited by the pre-defined rule set
and the seed error-corrected data. Experimental results demonstrate the
effectiveness of our approach and show that it can be combined with other
synthetic data sources to yield further improvements.
