Abstract
We describe two entries from the Cambridge University Engineering Department to the BEA 2019 Shared Task on grammatical error correction. Our submission to the low-resource track is based on prior work on using finite state transducers together with strong neural language models. Our system for the restricted track is a purely neural system consisting of neural language models and neural machine translation models trained with back-translation and a combination of checkpoint averaging and fine-tuning – without the help of any additional tools like spell checkers. The latter system has been used inside a separate system combination entry in cooperation with the Cambridge University Computer Lab.- Anthology ID:
- W19-4417
- 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:
- 168–175
- Language:
- URL:
- https://aclanthology.org/W19-4417
- DOI:
- 10.18653/v1/W19-4417
- Cite (ACL):
- Felix Stahlberg and Bill Byrne. 2019. The CUED’s Grammatical Error Correction Systems for BEA-2019. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 168–175, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- The CUED’s Grammatical Error Correction Systems for BEA-2019 (Stahlberg & Byrne, BEA 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W19-4417.pdf
- Data
- CoNLL-2014 Shared Task: Grammatical Error Correction