SEDNN: Shared and enhanced deep neural network model for cross-prompt automated essay scoring
SEDNN: Shared and enhanced deep neural network model for cross-prompt automated essay scoring
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DOI：
10.1016/j.knosys.2020.106491
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发表时间：
2020-12-27
影响因子：
8.8
通讯作者：
Nie, Jian-Yun
中科院分区：
文献类型：
--
作者：
Li, Xia;Chen, Minping;Nie, Jian-Yun
现有的自动作文评分（AES）的研究大多集中在单一提示，只有少数研究解决了交叉提示AES的问题。本文研究如何从多源提示中提取可传递的评分知识。与通常的方法不同，该方法从所有提示中提取一个不变的部分，我们提出将源提示中的所有数据进行融合，并提取它们与目标提示的共享知识，包括独立于源提示的特征和一些依赖于源提示的特征。我们表明，更多的评级知识可以提取和转移到目标提示使用此策略。然后使用所传输的模型来生成关于目标提示的一组伪训练数据，利用该伪训练数据为目标提示构建包含更多依赖于目标提示的特征的另一增强模型。实验结果表明，该改进模型能进一步提高最终性能，在交叉提示AES上的性能优于现有方法。(C)2020 Elsevier B.V.保留所有权利。
Most existing studies on Automated Essay Scoring (AES) focused on a single prompt, and only a few studies have addressed the problem of cross-prompt AES. This paper addresses the key question of how to extract more transferable rating knowledge from multiple source prompts. Different from the common approach which extracts an invariant part among all prompts, we propose to fuse all the data from the source prompts and to extract their shared knowledge with the target prompt, including prompt-independent features and some prompt-dependent features. We show that more rating knowledge can be extracted and transferred to the target prompt using this strategy. The transferred model is then used to generate a set of pseudo training data on the target prompt, with which another enhanced model incorporating more prompt-dependent features is built for the target prompt. Experiments show that this enhanced model can further improve the final performance and it outperforms the state-of-the-art methods on cross-prompt AES. (C) 2020 Elsevier B.V. All rights reserved.