@inproceedings{white-rozovskaya-2020-comparative,
title = "A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction",
author = "White, Max and
Rozovskaya, Alla",
editor = "Burstein, Jill and
Kochmar, Ekaterina and
Leacock, Claudia and
Madnani, Nitin and
Pil{\'a}n, Ildik{\'o} and
Yannakoudakis, Helen and
Zesch, Torsten",
booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = jul,
year = "2020",
address = "Seattle, WA, USA → Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.bea-1.21",
doi = "10.18653/v1/2020.bea-1.21",
pages = "198--208",
abstract = "Grammatical Error Correction (GEC) is concerned with correcting grammatical errors in written text. Current GEC systems, namely those leveraging statistical and neural machine translation, require large quantities of annotated training data, which can be expensive or impractical to obtain. This research compares techniques for generating synthetic data utilized by the two highest scoring submissions to the restricted and low-resource tracks in the BEA-2019 Shared Task on Grammatical Error Correction.",
}
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%0 Conference Proceedings
%T A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction
%A White, Max
%A Rozovskaya, Alla
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Yannakoudakis, Helen
%Y Zesch, Torsten
%S Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, WA, USA → Online
%F white-rozovskaya-2020-comparative
%X Grammatical Error Correction (GEC) is concerned with correcting grammatical errors in written text. Current GEC systems, namely those leveraging statistical and neural machine translation, require large quantities of annotated training data, which can be expensive or impractical to obtain. This research compares techniques for generating synthetic data utilized by the two highest scoring submissions to the restricted and low-resource tracks in the BEA-2019 Shared Task on Grammatical Error Correction.
%R 10.18653/v1/2020.bea-1.21
%U https://aclanthology.org/2020.bea-1.21
%U https://doi.org/10.18653/v1/2020.bea-1.21
%P 198-208
Markdown (Informal)
[A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction](https://aclanthology.org/2020.bea-1.21) (White & Rozovskaya, BEA 2020)
ACL