@inproceedings{lo-etal-2018-cool,
    title = "Cool {E}nglish: a Grammatical Error Correction System Based on Large Learner Corpora",
    author = "Lo, Yu-Chun  and
      Chen, Jhih-Jie  and
      Yang, Chingyu  and
      Chang, Jason",
    editor = "Zhao, Dongyan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/C18-2018/",
    pages = "82--85",
    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."
}