“Grammatical Error Correction With Neural Reinforcement Learning”, Keisuke Sakaguchi, Matt Post, Benjamin Van Durme2017-07-02 (imitation learning, model-free RL; backlinks; similar)⁠:

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.

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