神经网络还是语言特征? - 比较不同的机器学习方法来自动评估文本质量特征在L1和L2学习者的论证论文
Julian F Lohmann1, Fynn Junge1, Jens Möller1
1Institute for Psychology of Learning and Instruction, Kiel University, Olshausenstrasse 75, 24118 Kiel, Germany.
International journal of artificial intelligence in education
|September 24, 2025
概括
混合自动化作文评分模型结合语言特征和深度学习嵌入显示强的表现. 综合两种特征类型的混合模型在L1和L2论文中优于特征评分的单个资源模型.
科学领域:
- 自然语言处理自然语言处理.
- 教育技术的教育技术
- 计算语言学 计算语言学
背景情况:
- 自动化作文评分 (AES) 研究越来越多地利用混合模型,将特征工程和深度神经网络 (DNN) 结合起来.
- 现有的最先进的研究结果主要集中在整体评分任务上,使特征特定的评分得到更少的探索.
- 特征分数 (例如,内容,组织,语言质量) 提供了比整体分数更细致的反.
研究的目的:
- 为了比较基于特征,基于嵌入和混合模型对特征特定AES的有效性.
- 研究语言特征和深度学习嵌入在论文评分中的相互作用.
- 分析不同作文特征 (内容,组织,语言) 和学习者群体 (L1,L2) 的表现.
主要方法:
- 使用L1和L2的四个提示来训练特征特定的模型.
- 我们比较了三个模型变体:语言特征 (220个特征),DistilBERT嵌入和混合方法.
- 进行了加法和消去试验,以分析特定特征的特征相互作用.
主要成果:
- 在单个资源上训练时,基于特征的模型略高于基于嵌入的模型,特别是在组织特征上.
- 混合模型的表现始终优于单一资源模型,表明特征和嵌入式的互补信息.
- 特定特征 (形态复杂性,长度,词汇复杂性,错误/发生) 增强了基于嵌入的模型,用于特定的特征.
结论:
- 综合语言特征和深度学习嵌入的混合模型对特征特定的AES有效.
- 语言特征和嵌入式捕捉了文章质量的独特,互补的方面.
- 了解特征相互作用可以进一步优化针对性特征评估的AES模型.
相关概念视频
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01:10Higher Mental Functions of the Brain: Language
Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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01:28Lateralization
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01:16Language
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01:27Language and Cognition
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