Abstract
In this paper, we investigate the impact of using 4 recent neural models for generating artificial errors to help train the neural grammatical error correction models. We conduct a battery of experiments on the effect of data size, models, and comparison with a rule-based approach.- Anthology ID:
- W19-4449
- Volume:
- Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Helen Yannakoudakis, Ekaterina Kochmar, Claudia Leacock, Nitin Madnani, Ildikó Pilán, Torsten Zesch
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 478–483
- Language:
- URL:
- https://aclanthology.org/W19-4449
- DOI:
- 10.18653/v1/W19-4449
- Cite (ACL):
- Phu Mon Htut and Joel Tetreault. 2019. The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 478–483, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction (Htut & Tetreault, BEA 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/W19-4449.pdf