Minimally-Augmented Grammatical Error Correction

Roman Grundkiewicz, Marcin Junczys-Dowmunt


Abstract
There has been an increased interest in low-resource approaches to automatic grammatical error correction. We introduce Minimally-Augmented Grammatical Error Correction (MAGEC) that does not require any error-labelled data. Our unsupervised approach is based on a simple but effective synthetic error generation method based on confusion sets from inverted spell-checkers. In low-resource settings, we outperform the current state-of-the-art results for German and Russian GEC tasks by a large margin without using any real error-annotated training data. When combined with labelled data, our method can serve as an efficient pre-training technique
Anthology ID:
D19-5546
Volume:
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
357–363
Language:
URL:
https://www.aclweb.org/anthology/D19-5546
DOI:
10.18653/v1/D19-5546
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5546.pdf