@inproceedings{wang-etal-2023-system,
    title = "System Report for {CCL}23-Eval Task 8: {C}hinese Grammar Error Detection and Correction Using Multi-Granularity Information",
    author = "Wang, Yixuan  and
      Liu, Yijun  and
      Sun, Bo  and
      Che, Wanxiang",
    editor = "Sun, Maosong  and
      Qin, Bing  and
      Qiu, Xipeng  and
      Jiang, Jing  and
      Han, Xianpei",
    booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
    month = aug,
    year = "2023",
    address = "Harbin, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.ccl-3.30/",
    pages = "271--281",
    language = "eng",
    abstract = "``This paper introduces our system at CCL-2023 Task: Chinese Essay Fluency Evaluation (CEFE).The CEFE task aims to study the identification and correction of grammatical errors in primaryand middle school students' test compositions. The evaluation has three tracks to examine therecognition of wrong sentence types, character-level error correction, and wrong sentence rewrit-ing. According to the task characteristics and data distribution of each track, we propose a token-level discriminative model based on sequence labeling for the multi-label classification task ofwrong sentences, an auto-encoder model based on edited labels for character-level error correc-tion and a seq2seq model obtained by pre-training on pseudo data and fine-tuning on labeleddata to solve the wrong sentence rewriting task. In the final evaluation results, the method weproposed won the first place in all three tracks according to the corresponding evaluation metrics.''"
}