@inproceedings{qiu-etal-2019-improving,
title = "Improving Precision of Grammatical Error Correction with a Cheat Sheet",
author = "Qiu, Mengyang and
Chen, Xuejiao and
Liu, Maggie and
Parvathala, Krishna and
Patil, Apurva and
Park, Jungyeul",
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/W19-4425",
doi = "10.18653/v1/W19-4425",
pages = "240--245",
abstract = "In this paper, we explore two approaches of generating error-focused phrases and examine whether these phrases can lead to better performance in grammatical error correction for the restricted track of BEA 2019 Shared Task on GEC. Our results show that phrases directly extracted from GEC corpora outperform phrases from statistical machine translation phrase table by a large margin. Appending error+context phrases to the original GEC corpora yields comparably high precision. We also explore the generation of artificial syntactic error sentences using error+context phrases for the unrestricted track. The additional training data greatly facilitates syntactic error correction (e.g., verb form) and contributes to better overall performance.",
}
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%0 Conference Proceedings
%T Improving Precision of Grammatical Error Correction with a Cheat Sheet
%A Qiu, Mengyang
%A Chen, Xuejiao
%A Liu, Maggie
%A Parvathala, Krishna
%A Patil, Apurva
%A Park, Jungyeul
%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 qiu-etal-2019-improving
%X In this paper, we explore two approaches of generating error-focused phrases and examine whether these phrases can lead to better performance in grammatical error correction for the restricted track of BEA 2019 Shared Task on GEC. Our results show that phrases directly extracted from GEC corpora outperform phrases from statistical machine translation phrase table by a large margin. Appending error+context phrases to the original GEC corpora yields comparably high precision. We also explore the generation of artificial syntactic error sentences using error+context phrases for the unrestricted track. The additional training data greatly facilitates syntactic error correction (e.g., verb form) and contributes to better overall performance.
%R 10.18653/v1/W19-4425
%U https://aclanthology.org/W19-4425
%U https://doi.org/10.18653/v1/W19-4425
%P 240-245
Markdown (Informal)
[Improving Precision of Grammatical Error Correction with a Cheat Sheet](https://aclanthology.org/W19-4425) (Qiu et al., BEA 2019)
ACL
- Mengyang Qiu, Xuejiao Chen, Maggie Liu, Krishna Parvathala, Apurva Patil, and Jungyeul Park. 2019. Improving Precision of Grammatical Error Correction with a Cheat Sheet. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 240–245, Florence, Italy. Association for Computational Linguistics.