The field of Grammatical Error Correction (GEC) has produced various systems to deal with focused phenomena or general text editing. We propose an automatic way to combine black-box systems. Our method automatically detects the strength of a system or the combination of several systems per error type, improving precision and recall while optimizing F-score directly. We show consistent improvement over the best standalone system in all the configurations tested. This approach also outperforms average ensembling of different RNN models with random initializations. In addition, we analyze the use of BERT for GEC - reporting promising results on this end. We also present a spellchecker created for this task which outperforms standard spellcheckers tested on the task of spellchecking. This paper describes a system submission to Building Educational Applications 2019 Shared Task: Grammatical Error Correction. Combining the output of top BEA 2019 shared task systems using our approach, currently holds the highest reported score in the open phase of the BEA 2019 shared task, improving F-0.5 score by 3.7 points over the best result reported.
@inproceedings{DBLP:conf/bea/KantorKCCLTMS19,
author = {Yoav Kantor and
Yoav Katz and
\textbf{\emph{Leshem Choshen*}} and
Edo Cohen{-}Karlik and
Naftali Liberman and
Assaf Toledo and
Amir Menczel and
Noam Slonim},
editor = {Helen Yannakoudakis and
Ekaterina Kochmar and
Claudia Leacock and
Nitin Madnani and
Ildik{\'{o}} Pil{\'{a}}n and
Torsten Zesch},
title = {Learning to combine Grammatical Error Corrections},
booktitle = {Proceedings of the Fourteenth Workshop on Innovative Use of {NLP}
for Building Educational Applications, BEA@ACL 2019, Florence, Italy,
August 2, 2019},
pages = {139--148},
publisher = {Association for Computational Linguistics},
year = {2019},
url = {https://doi.org/10.18653/v1/w19-4414},
doi = {10.18653/V1/W19-4414},
timestamp = {Fri, 06 Aug 2021 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/bea/KantorKCCLTMS19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.