Improving Precision of Grammatical Error Correction with a Cheat Sheet

Mengyang Qiu, Xuejiao Chen, Maggie Liu, Krishna Parvathala, Apurva Patil, Jungyeul Park

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.
Anthology ID:
W19-4425
Volume:
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Helen Yannakoudakis, Ekaterina Kochmar, Claudia Leacock, Nitin Madnani, Ildikó Pilán, Torsten Zesch
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
240–245
Language:
URL:
https://aclanthology.org/W19-4425
DOI:
10.18653/v1/W19-4425
Bibkey:
Cite (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.
Cite (Informal):
Improving Precision of Grammatical Error Correction with a Cheat Sheet (Qiu et al., BEA 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/W19-4425.pdf
PDF Search