  Grammatical error correction (GEC) suffers from a lack of sufficient parallel
data. Therefore, GEC studies have developed various methods to generate pseudo
data, which comprise pairs of grammatical and artificially produced
ungrammatical sentences. Currently, a mainstream approach to generate pseudo
data is back-translation (BT). Most previous GEC studies using BT have employed
the same architecture for both GEC and BT models. However, GEC models have
different correction tendencies depending on their architectures. Thus, in this
study, we compare the correction tendencies of the GEC models trained on pseudo
data generated by different BT models, namely, Transformer, CNN, and LSTM. The
results confirm that the correction tendencies for each error type are
different for every BT model. Additionally, we examine the correction
tendencies when using a combination of pseudo data generated by different BT
models. As a result, we find that the combination of different BT models
improves or interpolates the F_0.5 scores of each error type compared with that
of single BT models with different seeds.
