Grammatical Error Correction with Neural Reinforcement Learning

international joint conference on natural language processing, 2017.

Cited by: 7|Bibtex|Views14
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

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 demonst...More

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