System Report for CCL24-Eval Task 7: Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation
<|reference_start|>System Report for CCL24-Eval Task 7: Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation: 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 pre-training 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.<|reference_end|>
@article{zhang2024system,
    title={System Report for CCL24-Eval Task 7: Multi-Error Modeling and
  Fluency-Targeted Pre-training for Chinese Essay Evaluation},
    author={Jingshen Zhang, Xiangyu Yang, Xinkai Su, Xinglu Chen, Tianyou Huang,
  Xinying Qiu},
    journal={arXiv preprint arXiv:2407.08206},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08206},
    primaryClass={cs.CL}
}