Grammatical Error Correction with Neural Reinforcement Learning

Keisuke Sakaguchi; Matt Post; Benjamin Van Durme

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arXiv · 1707.00299

Grammatical Error Correction with Neural Reinforcement Learning

Keisuke Sakaguchi·Matt Post·Benjamin Van Durme

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Abstract

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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Keisuke Sakaguchi, Matt Post, Benjamin Van Durme. 2017-07-02. Grammatical Error Correction with Neural Reinforcement Learning. https://arxiv.org/abs/1707.00299

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