@inproceedings{liao-etal-2017-ynu,
    title = "{YNU}-{HPCC} at {IJCNLP}-2017 Task 1: {C}hinese Grammatical Error Diagnosis Using a Bi-directional {LSTM}-{CRF} Model",
    author = "Liao, Quanlei  and
      Wang, Jin  and
      Yang, Jinnan  and
      Zhang, Xuejie",
    editor = "Liu, Chao-Hong  and
      Nakov, Preslav  and
      Xue, Nianwen",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://preview.aclanthology.org/ingest_wac_2008/I17-4011/",
    pages = "73--77",
    abstract = "Building a system to detect Chinese grammatical errors is a challenge for natural-language processing researchers. As Chinese learners are increasing, developing such a system can help them study Chinese more easily. This paper introduces a bi-directional long short-term memory (BiLSTM) - conditional random field (CRF) model to produce the sequences that indicate an error type for every position of a sentence, since we regard Chinese grammatical error diagnosis (CGED) as a sequence-labeling problem."
}