Neural Sequence-Labelling Models for Grammatical Error Correction
Helen Yannakoudakis, Marek Rei, Øistein E. Andersen, Zheng Yuan
Abstract
We propose an approach to N-best list reranking using neural sequence-labelling models. We train a compositional model for error detection that calculates the probability of each token in a sentence being correct or incorrect, utilising the full sentence as context. Using the error detection model, we then re-rank the N best hypotheses generated by statistical machine translation systems. Our approach achieves state-of-the-art results on error correction for three different datasets, and it has the additional advantage of only using a small set of easily computed features that require no linguistic input.- Anthology ID:
- D17-1297
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2795–2806
- Language:
- URL:
- https://aclanthology.org/D17-1297
- DOI:
- 10.18653/v1/D17-1297
- Cite (ACL):
- Helen Yannakoudakis, Marek Rei, Øistein E. Andersen, and Zheng Yuan. 2017. Neural Sequence-Labelling Models for Grammatical Error Correction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2795–2806, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Neural Sequence-Labelling Models for Grammatical Error Correction (Yannakoudakis et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/D17-1297.pdf
- Data
- FCE, JFLEG