基于大模型数据增强的作文流畅性评价方法

Qianwen Peng; Yanzipeng Gao; Xiaoqing Li; Fanke Min; Mingrui Li; Zhichun Wang (王志春); Tianyun Liu

基于大模型数据增强的作文流畅性评价方法

Qianwen Peng (彭倩雯), Yanzipeng Gao (高延子鹏), Xiaoqing Li (李晓青), Fanke Min (闵凡珂), Mingrui Li (李明锐), Zhichun Wang (王志春), Tianyun Liu (刘天昀)

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Abstract

“CCL2024-Eval任务7为中小学生作文流畅性评价 (Chinese Essay Fluency Evalua-tion,CEFE),该任务定义了三项重要且富有挑战性的问题,包括中小学作文病句类型识别、中小学作文病句改写、以及中小学作文流畅性评级。本队伍参加了评测任务7的三项子任务,分别获得了45.19、43.90和45.84的得分。本报告详细介绍本队伍在三个子任务上采用的技术方法,并对评测结果进行分析。”

Anthology ID:: 2024.ccl-3.33
Volume:: Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
Month:: July
Year:: 2024
Address:: Taiyuan, China
Editors:: Lin Hongfei, Tan Hongye, Li Bin
Venue:: CCL
SIG:
Publisher:: Chinese Information Processing Society of China
Note:
Pages:: 294–301
Language:: Chinese
URL:: https://aclanthology.org/2024.ccl-3.33/
DOI:
Bibkey:
Cite (ACL):: Qianwen Peng, Yanzipeng Gao, Xiaoqing Li, Fanke Min, Mingrui Li, Zhichun Wang, and Tianyun Liu. 2024. 基于大模型数据增强的作文流畅性评价方法. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 294–301, Taiyuan, China. Chinese Information Processing Society of China.
Cite (Informal):: 基于大模型数据增强的作文流畅性评价方法 (Peng et al., CCL 2024)
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PDF:: https://aclanthology.org/2024.ccl-3.33.pdf

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@inproceedings{qianwen-etal-2024-ji,
    title = "基于大模型数据增强的作文流畅性评价方法",
    author = "Peng, Qianwen  and
      Gao, Yanzipeng  and
      Li, Xiaoqing  and
      Min, Fanke  and
      Li, Mingrui  and
      Wang, Zhichun  and
      Liu, Tianyun",
    editor = "Hongfei, Lin  and
      Hongye, Tan  and
      Bin, Li",
    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://aclanthology.org/2024.ccl-3.33/",
    pages = "294--301",
    language = "zho",
    abstract = "``CCL2024-Eval任 务7为 中 小 学 生 作 文 流 畅 性 评 价 (Chinese Essay Fluency Evalua-tion,CEFE),该任务定义了三项重要且富有挑战性的问题,包括中小学作文病句类型识别、中小学作文病句改写、以及中小学作文流畅性评级。本队伍参加了评测任务7的三项子任务,分别获得了45.19、43.90和45.84的得分。本报告详细介绍本队伍在三个子任务上采用的技术方法,并对评测结果进行分析。''"
}

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%0 Conference Proceedings
%T 基于大模型数据增强的作文流畅性评价方法
%A Peng, Qianwen
%A Gao, Yanzipeng
%A Li, Xiaoqing
%A Min, Fanke
%A Li, Mingrui
%A Wang, Zhichun
%A Liu, Tianyun
%Y Hongfei, Lin
%Y Hongye, Tan
%Y Bin, Li
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F qianwen-etal-2024-ji
%X “CCL2024-Eval任 务7为 中 小 学 生 作 文 流 畅 性 评 价 (Chinese Essay Fluency Evalua-tion,CEFE),该任务定义了三项重要且富有挑战性的问题,包括中小学作文病句类型识别、中小学作文病句改写、以及中小学作文流畅性评级。本队伍参加了评测任务7的三项子任务,分别获得了45.19、43.90和45.84的得分。本报告详细介绍本队伍在三个子任务上采用的技术方法,并对评测结果进行分析。”
%U https://aclanthology.org/2024.ccl-3.33/
%P 294-301

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Markdown (Informal)

[基于大模型数据增强的作文流畅性评价方法](https://aclanthology.org/2024.ccl-3.33/) (Peng et al., CCL 2024)

基于大模型数据增强的作文流畅性评价方法 (Peng et al., CCL 2024)

ACL

Qianwen Peng, Yanzipeng Gao, Xiaoqing Li, Fanke Min, Mingrui Li, Zhichun Wang, and Tianyun Liu. 2024. 基于大模型数据增强的作文流畅性评价方法. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 294–301, Taiyuan, China. Chinese Information Processing Society of China.