CCL25-Eval 任务6系统报告:基于数据增强及大小模型协同的中小学作文修辞识别

Xuquan Zong; Jiyuan An; Xiang Fu; Luming Lu; Haonan Zhu; Liner Yang; Erhong Yang

CCL25-Eval 任务6系统报告:基于数据增强及大小模型协同的中小学作文修辞识别

Xuquan Zong, Jiyuan An, Xiang Fu, Luming Lu, Haonan Zhu, Liner Yang, Erhong Yang

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Abstract

"CCL25-Eval任务6提出了一个段落级、多层次,细粒度中小学修辞识别与理解任务。针对修辞分类任务的特点,本文构建了一种以数据增强为核心、结合高效监督微调的多策略融合框架,并融合语句层面修辞识别与段落句间关系建模及识别,以全面提升模型的修辞理解能力。针对修辞成分抽取任务的特点,本文采用先进行修辞类别判定,后在该基础上进行修辞相关实体识别的两阶段处理策略,有效提升了整体识别精度。结果表明,本文所提出的方法能够有效对修辞进行识别和抽取,三个赛道上的分数分别达到了43.47、51.71、38.27,总成绩位列第二。"

Anthology ID:: 2025.ccl-2.28
Volume:: Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
Month:: August
Year:: 2025
Address:: Jinan, China
Editors:: Hongfei Lin, Bin Li, Hongye Tan
Venue:: CCL
SIG:
Publisher:: Chinese Information Processing Society of China
Note:
Pages:: 233–243
Language:
URL:: https://aclanthology.org/2025.ccl-2.28/
DOI:
Bibkey:
Cite (ACL):: Xuquan Zong, Jiyuan An, Xiang Fu, Luming Lu, Haonan Zhu, Liner Yang, and Erhong Yang. 2025. CCL25-Eval 任务6系统报告:基于数据增强及大小模型协同的中小学作文修辞识别. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 233–243, Jinan, China. Chinese Information Processing Society of China.
Cite (Informal):: CCL25-Eval 任务6系统报告:基于数据增强及大小模型协同的中小学作文修辞识别 (Zong et al., CCL 2025)
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PDF:: https://aclanthology.org/2025.ccl-2.28.pdf

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@inproceedings{zong-etal-2025-ccl25,
    title = "{CCL}25-Eval 任务6系统报告:基于数据增强及大小模型协同的中小学作文修辞识别",
    author = "Zong, Xuquan  and
      An, Jiyuan  and
      Fu, Xiang  and
      Lu, Luming  and
      Zhu, Haonan  and
      Yang, Liner  and
      Yang, Erhong",
    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://aclanthology.org/2025.ccl-2.28/",
    pages = "233--243",
    abstract = "``CCL25-Eval任务6提出了一个段落级、多层次,细粒度中小学修辞识别与理解任务。针对修辞分类任务的特点,本文构建了一种以数据增强为核心、结合高效监督微调的多策略融合框架,并融合语句层面修辞识别与段落句间关系建模及识别,以全面提升模型的修辞理解能力。针对修辞成分抽取任务的特点,本文采用先进行修辞类别判定,后在该基础上进行修辞相关实体识别的两阶段处理策略,有效提升了整体识别精度。结果表明,本文所提出的方法能够有效对修辞进行识别和抽取,三个赛道上的分数分别达到了43.47、51.71、38.27,总成绩位列第二。''"
}

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    <abstract>“CCL25-Eval任务6提出了一个段落级、多层次,细粒度中小学修辞识别与理解任务。针对修辞分类任务的特点,本文构建了一种以数据增强为核心、结合高效监督微调的多策略融合框架,并融合语句层面修辞识别与段落句间关系建模及识别,以全面提升模型的修辞理解能力。针对修辞成分抽取任务的特点,本文采用先进行修辞类别判定,后在该基础上进行修辞相关实体识别的两阶段处理策略,有效提升了整体识别精度。结果表明,本文所提出的方法能够有效对修辞进行识别和抽取,三个赛道上的分数分别达到了43.47、51.71、38.27,总成绩位列第二。”</abstract>
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%0 Conference Proceedings
%T CCL25-Eval 任务6系统报告:基于数据增强及大小模型协同的中小学作文修辞识别
%A Zong, Xuquan
%A An, Jiyuan
%A Fu, Xiang
%A Lu, Luming
%A Zhu, Haonan
%A Yang, Liner
%A Yang, Erhong
%Y Lin, Hongfei
%Y Li, Bin
%Y Tan, Hongye
%S Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
%D 2025
%8 August
%I Chinese Information Processing Society of China
%C Jinan, China
%F zong-etal-2025-ccl25
%X “CCL25-Eval任务6提出了一个段落级、多层次,细粒度中小学修辞识别与理解任务。针对修辞分类任务的特点,本文构建了一种以数据增强为核心、结合高效监督微调的多策略融合框架,并融合语句层面修辞识别与段落句间关系建模及识别,以全面提升模型的修辞理解能力。针对修辞成分抽取任务的特点,本文采用先进行修辞类别判定,后在该基础上进行修辞相关实体识别的两阶段处理策略,有效提升了整体识别精度。结果表明,本文所提出的方法能够有效对修辞进行识别和抽取,三个赛道上的分数分别达到了43.47、51.71、38.27,总成绩位列第二。”
%U https://aclanthology.org/2025.ccl-2.28/
%P 233-243

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Markdown (Informal)

[CCL25-Eval 任务6系统报告:基于数据增强及大小模型协同的中小学作文修辞识别](https://aclanthology.org/2025.ccl-2.28/) (Zong et al., CCL 2025)

CCL25-Eval 任务6系统报告:基于数据增强及大小模型协同的中小学作文修辞识别 (Zong et al., CCL 2025)

ACL

Xuquan Zong, Jiyuan An, Xiang Fu, Luming Lu, Haonan Zhu, Liner Yang, and Erhong Yang. 2025. CCL25-Eval 任务6系统报告:基于数据增强及大小模型协同的中小学作文修辞识别. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 233–243, Jinan, China. Chinese Information Processing Society of China.