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

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


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
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
82–85
Language:
URL:
https://www.aclweb.org/anthology/C18-2018
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C18-2018.pdf