Abstract
n this work we adapt machine translation (MT) to grammatical error correction, identifying how components of the statistical MT pipeline can be modified for this task and analyzing how each modification impacts system performance. We evaluate the contribution of each of these components with standard evaluation metrics and automatically characterize the morphological and lexical transformations made in system output. Our model rivals the current state of the art using a fraction of the training data.- Anthology ID:
- W17-5039
- Volume:
- Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Joel Tetreault, Jill Burstein, Claudia Leacock, Helen Yannakoudakis
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 345–356
- Language:
- URL:
- https://aclanthology.org/W17-5039
- DOI:
- 10.18653/v1/W17-5039
- Cite (ACL):
- Courtney Napoles and Chris Callison-Burch. 2017. Systematically Adapting Machine Translation for Grammatical Error Correction. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 345–356, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Systematically Adapting Machine Translation for Grammatical Error Correction (Napoles & Callison-Burch, BEA 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W17-5039.pdf
- Code
- cnap/smt-for-gec
- Data
- FCE, JFLEG