  Due to the lack of parallel data in current Grammatical Error Correction
(GEC) task, models based on Sequence to Sequence framework cannot be adequately
trained to obtain higher performance. We propose two data synthesis methods
which can control the error rate and the ratio of error types on synthetic
data. The first approach is to corrupt each word in the monolingual corpus with
a fixed probability, including replacement, insertion and deletion. Another
approach is to train error generation models and further filtering the decoding
results of the models. The experiments on different synthetic data show that
the error rate is 40% and the ratio of error types is the same can improve the
model performance better. Finally, we synthesize about 100 million data and
achieve comparable performance as the state of the art, which uses twice as
much data as we use.
