Document-level grammatical error correction

Zheng Yuan, Christopher Bryant


Abstract
Document-level context can provide valuable information in grammatical error correction (GEC), which is crucial for correcting certain errors and resolving inconsistencies. In this paper, we investigate context-aware approaches and propose document-level GEC systems. Additionally, we employ a three-step training strategy to benefit from both sentence-level and document-level data. Our system outperforms previous document-level and all other NMT-based single-model systems, achieving state of the art on a common test set.
Anthology ID:
2021.bea-1.8
Volume:
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
Month:
April
Year:
2021
Address:
Online
Editors:
Jill Burstein, Andrea Horbach, Ekaterina Kochmar, Ronja Laarmann-Quante, Claudia Leacock, Nitin Madnani, Ildikó Pilán, Helen Yannakoudakis, Torsten Zesch
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
75–84
Language:
URL:
https://aclanthology.org/2021.bea-1.8
DOI:
Bibkey:
Cite (ACL):
Zheng Yuan and Christopher Bryant. 2021. Document-level grammatical error correction. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 75–84, Online. Association for Computational Linguistics.
Cite (Informal):
Document-level grammatical error correction (Yuan & Bryant, BEA 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.bea-1.8.pdf
Code
 chrisjbryant/doc-gec