@inproceedings{tang-tang-2025-system-report,
    title = "System Report for {CCL}25-Eval Task 6: Enhancing {C}hinese Essay Rhetoric Recognition through Targeted Data Augmentation and Model Ensemble Voting",
    author = "Tang, Jingjun  and
      Tang, Zhiwen",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-2.29/",
    pages = "244--251",
    abstract = "``This paper presents our approach to the Second Chinese Essay Rhetoric Identification and Understanding Competition, which focuses on analyzing rhetorical features in essays written by primary and secondary school students. The competition includes three tasks: multi-label classification of rhetorical forms, divided into 9 coarse-grained and 19 fine-grained categories; multi-label classification of rhetorical content, comprising 5 coarse-grained and 11 fine-grained categories specific to certain rhetorical types; and extraction of rhetorical components, including connectives, descriptive objects, and specific rhetorical content. To address the challenge of limited training data, we applied targeted data augmentation and manual corrections to build a high-quality dataset. We then fine-tuned large language models using one-shot and in-context learning. Finally, we employed an ensemble strategy that integrates model predictions through a voting mechanism. Our system achieved a score of 52.78 and ranked third in the competition.''"
}