@inproceedings{kantor-etal-2019-learning,
title = "Learning to combine Grammatical Error Corrections",
author = "Kantor, Yoav and
Katz, Yoav and
Choshen, Leshem and
Cohen-Karlik, Edo and
Liberman, Naftali and
Toledo, Assaf and
Menczel, Amir and
Slonim, Noam",
editor = "Yannakoudakis, Helen and
Kochmar, Ekaterina and
Leacock, Claudia and
Madnani, Nitin and
Pil{\'a}n, Ildik{\'o} and
Zesch, Torsten",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4414",
doi = "10.18653/v1/W19-4414",
pages = "139--148",
abstract = "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.",
}
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%0 Conference Proceedings
%T Learning to combine Grammatical Error Corrections
%A Kantor, Yoav
%A Katz, Yoav
%A Choshen, Leshem
%A Cohen-Karlik, Edo
%A Liberman, Naftali
%A Toledo, Assaf
%A Menczel, Amir
%A Slonim, Noam
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F kantor-etal-2019-learning
%X 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.
%R 10.18653/v1/W19-4414
%U https://aclanthology.org/W19-4414
%U https://doi.org/10.18653/v1/W19-4414
%P 139-148
Markdown (Informal)
[Learning to combine Grammatical Error Corrections](https://aclanthology.org/W19-4414) (Kantor et al., BEA 2019)
ACL
- Yoav Kantor, Yoav Katz, Leshem Choshen, Edo Cohen-Karlik, Naftali Liberman, Assaf Toledo, Amir Menczel, and Noam Slonim. 2019. Learning to combine Grammatical Error Corrections. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 139–148, Florence, Italy. Association for Computational Linguistics.