Enhanced cross-prompt trait scoring via syntactic feature fusion and contrastive learning
Enhanced cross-prompt trait scoring via syntactic feature fusion and contrastive learning
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DOI：
10.1007/s11227-023-05640-2
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发表时间：
2023-09
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影响因子：
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通讯作者：
Jingbo Sun;Weiming Peng;T. Song;Haitao Liu;Shuqin Zhu;Jihua Song
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作者：
Jingbo Sun;Weiming Peng;T. Song;Haitao Liu;Shuqin Zhu;Jihua Song
自动论文评分旨在自动评估论文的质量。它是自然语言处理领域的主要教育应用之一。近年来，研究范围已从单纯的专项评分扩展到交叉提示评分，并进一步集中于对不同性状的评分。然而，交叉提示特质评分需要识别内部关系，领域知识和特质表征，以及处理特定特质的训练数据不足。为了解决这些问题，我们提出了一个RDCTS模型，采用对比学习，并利用Kullback-Leibler分歧来衡量积极和消极的样本的相似性，我们设计了一个特征融合算法，结合POS和语法特征，而不是使用单一的文本属性特征作为输入的神经AES系统。我们通过在层次模型的单词级和句子级添加dropout层来合并隐式数据增强，以减轻有限数据的影响。实验结果表明，我们的RDCTS达到国家的最先进的性能和更大的一致性。
Automated essay scoring aims to evaluate the quality of an essay automatically. It is one of the main educational applications in the field of natural language processing. Recently, the research scope has been extended from prompt-special scoring to cross-prompt scoring and further concentrating on scoring different traits. However, cross-prompt trait scoring requires identifying inner-relations, domain knowledge, and trait representation as well as dealing with insufficient training data for the specific traits. To address these problems, we propose a RDCTS model that employs contrastive learning and utilizes Kullback–Leibler divergence to measure the similarity of positive and negative samples, and we design a feature fusion algorithm that combines POS and syntactic features instead of using single text attribute features as input for the neural AES system. We incorporate implicit data augmentation by adding the dropout layer to the word level and sentence level of the hierarchical model to mitigate the effects of limited data. Experimental results show that our RDCTS achieves state-of-the-art performance and greater consistency.