Cool English: a Grammatical Error Correction System Based on Large Learner Corpora

Yu-Chun Lo, Jhih-Jie Chen, Chingyu Yang, Jason Chang

[How to correct problems with metadata yourself]

Abstract
This paper presents a grammatical error correction (GEC) system that provides corrective feedback for essays. We apply the sequence-to-sequence model, which is frequently used in machine translation and text summarization, to this GEC task. The model is trained by EF-Cambridge Open Language Database (EFCAMDAT), a large learner corpus annotated with grammatical errors and corrections. Evaluation shows that our system achieves competitive performance on a number of publicly available testsets.
Anthology ID:
C18-2018
Volume:
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico
Editor:
Dongyan Zhao
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
82–85
Language:
URL:
https://aclanthology.org/C18-2018
DOI:
Bibkey:
Cite (ACL):
Yu-Chun Lo, Jhih-Jie Chen, Chingyu Yang, and Jason Chang. 2018. Cool English: a Grammatical Error Correction System Based on Large Learner Corpora. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 82–85, Santa Fe, New Mexico. Association for Computational Linguistics.
Cite (Informal):
Cool English: a Grammatical Error Correction System Based on Large Learner Corpora (Lo et al., COLING 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/C18-2018.pdf
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