@inproceedings{grundkiewicz-junczys-dowmunt-2019-minimally,
title = "Minimally-Augmented Grammatical Error Correction",
author = "Grundkiewicz, Roman and
Junczys-Dowmunt, Marcin",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5546",
doi = "10.18653/v1/D19-5546",
pages = "357--363",
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",
}
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%0 Conference Proceedings
%T Minimally-Augmented Grammatical Error Correction
%A Grundkiewicz, Roman
%A Junczys-Dowmunt, Marcin
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F grundkiewicz-junczys-dowmunt-2019-minimally
%X 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
%R 10.18653/v1/D19-5546
%U https://aclanthology.org/D19-5546
%U https://doi.org/10.18653/v1/D19-5546
%P 357-363
Markdown (Informal)
[Minimally-Augmented Grammatical Error Correction](https://aclanthology.org/D19-5546) (Grundkiewicz & Junczys-Dowmunt, WNUT 2019)
ACL
- Roman Grundkiewicz and Marcin Junczys-Dowmunt. 2019. Minimally-Augmented Grammatical Error Correction. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 357–363, Hong Kong, China. Association for Computational Linguistics.