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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.bea-1.8.pdf
- Code
- chrisjbryant/doc-gec