@inproceedings{jingshen-etal-2024-multi,
    title = "Multi-Error Modeling and Fluency-Targeted Pre-training for {C}hinese Essay Evaluation",
    author = "Jingshen, Zhang  and
      Xiangyu, Yang  and
      Xinkai, Su  and
      Xinglu, Chen  and
      Tianyou, Huang  and
      Xinying, Qiu",
    editor = "Lin, Hongfei  and
      Tan, Hongye  and
      Li, Bin",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.ccl-3.30/",
    pages = "269--277",
    language = "eng",
    abstract = "``This system report presents our approaches and results for the Chinese Essay Fluency Evaluation (CEFE) task at CCL-2024. For Track 1, we optimized predictions for challenging fine-grained error types using binary classification models and trained coarse-grained models on the Chinese Learner 4W corpus. In Track 2, we enhanced performance by constructing a pseudo-dataset with multiple error types per sentence. For Track 3, where we achieved first place, we generated fluency-rated pseudo-data via back-translation for pretraining and used an NSP-based strategy with Symmetric Cross Entropy loss to capture context and mitigate long dependencies. Our methods effectively address key challenges in Chinese Essay Fluency Evaluation.''"
}