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.
@article{arxiv.1707.00299,
title = {Grammatical Error Correction with Neural Reinforcement Learning},
author = {Keisuke Sakaguchi and Matt Post and Benjamin Van Durme},
journal= {arXiv preprint arXiv:1707.00299},
year = {2017}
}