  We propose a neural encoder-decoder model with reinforcement learning (NRL)
for grammatical error correction (GEC). Unlike conventional maximum likelihood
estimation (MLE), the model directly optimizes towards an objective that
considers a sentence-level, task-specific evaluation metric, avoiding the
exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in
human and automated evaluation metrics, achieving the state-of-the-art on a
fluency-oriented GEC corpus.
