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Exploring structure-aware representation learning for automated essay scoring

delete2025-07-08
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PRE
AI
K
Kaiwei Cai
L
Li Kong
J
Junsheng Zhou *
梁丹丹 封面图
梁丹丹 (Dandan Liang)
DOI：10.1007/s10115-025-02530-2delete
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摘要

摘要

En 中文
自动作文评分（AES）旨在根据写作质量预测作文的分数。一个有说服力的作文结构能促进良好的表达和句子间的逻辑语义连贯，这对于整合和理解上下文至关重要，且应始终从教师评分的角度加以考虑。然而，现有的结构学习方法要么是特定体裁的，要么结构浅显，无法很好地泛化到不同体裁的作文中，也无法揭示作文中潜在的丰富结构。事实上，考察句子间的潜在交互能够推进作文结构的构建与理解。基于此动机，我们提出了自适应结构感知图神经网络，以探索作文的结构感知表示学习，实现更全面的特征以更好地表征作文。在Automated Student Assessment Prize和Chinese Argumentative Essays数据集上的广泛实验结果证明了我们方法的有效性。我们还设计了不同设置下的实验，表明我们的方法仅依赖分数标签，即可在多种作文体裁和主题上表现良好。
Keyword:
Automated essay scoring
Graph neural network
Representation learning
Structure learning

期刊

Knowledge and Information Systems 封面图
Knowledge and Information Systems
IF:
3.1
论文数:
579
被引数:
5.2K

机构

C
Chinese Language and Literature
学者数:
9
论文数: 8
被引数: 0
S
School of Computer and Electronic Information
学者数:
34
论文数: 16
被引数: 0
引用论文

引用论文

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Automatic Text Scoring Using Neural Networks
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err2016-01-01
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