  Grammar Error Correction(GEC) mainly relies on the availability of high
quality of large amount of synthetic parallel data of grammatically correct and
erroneous sentence pairs. The quality of the synthetic data is evaluated on how
well the GEC system performs when pre-trained using it. But this does not
provide much insight into what are the necessary factors which define the
quality of these data. So this work aims to introduce 3 metrics - reliability,
diversity and distribution match to provide more insight into the quality of
large-scale synthetic data generated for the GEC task, as well as automatically
evaluate them. Evaluating these three metrics automatically can also help in
providing feedback to the data generation systems and thereby improve the
quality of the synthetic data generated dynamically
