This paper investigates how to effectively incorporate a pre-trained masked
language model (MLM), such as BERT, into an encoder-decoder (EncDec) model for
grammatical error correction (GEC). The answer to this question is not as
straightforward as one might expect because the previous common methods for
incorporating a MLM into an EncDec model have potential drawbacks when applied
to GEC. For example, the distribution of the inputs to a GEC model can be
considerably different (erroneous, clumsy, etc.) from that of the corpora used
for pre-training MLMs; however, this issue is not addressed in the previous
methods. Our experiments show that our proposed method, where we first
fine-tune a MLM with a given GEC corpus and then use the output of the
fine-tuned MLM as additional features in the GEC model, maximizes the benefit
of the MLM. The best-performing model achieves state-of-the-art performances on
the BEA-2019 and CoNLL-2014 benchmarks. Our code is publicly available at:
https://github.com/kanekomasahiro/bert-gec.