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.- Anthology ID:
- I17-2062
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 366–372
- Language:
- URL:
- https://aclanthology.org/I17-2062
- DOI:
- Cite (ACL):
- Keisuke Sakaguchi, Matt Post, and Benjamin Van Durme. 2017. Grammatical Error Correction with Neural Reinforcement Learning. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 366–372, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Grammatical Error Correction with Neural Reinforcement Learning (Sakaguchi et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/I17-2062.pdf
- Data
- FCE, JFLEG