The CUED’s Grammatical Error Correction Systems for BEA-2019

Felix Stahlberg, Bill Byrne


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
Venues:
ACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
168–175
URL:
https://www.aclweb.org/anthology/W19-4417
DOI:
10.18653/v1/W19-4417
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PDF:
https://www.aclweb.org/anthology/W19-4417.pdf