arXiv 2206.082322022-06-16cs.CLcs.AI

用于自动论文评分的深度学习架构

Deep Learning Architecture for Automatic Essay Scoring

Tsegaye Misikir Tashu, Chandresh Kumar Maurya, Tomas Horvath

摘要：

随着在线学习和评价平台（如Coursera、Udemy、Khan academy等）的兴起，论文自动评价（AES，Automatic Evaluation of essay）成为一个严峻的问题，研究人员提出了许多自动评价技术。然而，这些技术中的许多使用手工制作的特征，因此从特征表示的角度来看是有限的。深度学习已经成为机器学习中的一种新范式，它可以利用大量数据并识别对论文评估有用的特征。为此，我们提出了一种新的架构，基于递归网络（RNN）和卷积神经网络（CNN）。在所提出的架构中，多通道卷积层从词嵌入向量和基本语义概念中学习和捕获词n-gram的上下文特征，以使用最大池化操作形成文章级别的特征向量。RNN的一个变体称为双门循环单元（BGRU），用于访问先前和随后的上下文表示。实验是在Kaggle上提供的八个数据集上进行的，用于AES任务。实验结果表明，我们提出的系统实现了显着更高的分级准确性比其他深度学习为基础的AES系统，也是其他国家的最先进的AES系统。

英文摘要：

Automatic evaluation of essay (AES) and also called automatic essay scoring has become a severe problem due to the rise of online learning and evaluation platforms such as Coursera, Udemy, Khan academy, and so on. Researchers have recently proposed many techniques for automatic evaluation. However, many of these techniques use hand-crafted features and thus are limited from the feature representation point of view. Deep learning has emerged as a new paradigm in machine learning which can exploit the vast data and identify the features useful for essay evaluation. To this end, we propose a novel architecture based on recurrent networks (RNN) and convolution neural network (CNN). In the proposed architecture, the multichannel convolutional layer learns and captures the contextual features of the word n-gram from the word embedding vectors and the essential semantic concepts to form the feature vector at essay level using max-pooling operation. A variant of RNN called Bi-gated recurrent unit (BGRU) is used to access both previous and subsequent contextual representations. The experiment was carried out on eight data sets available on Kaggle for the task of AES. The experimental results show that our proposed system achieves significantly higher grading accuracy than other deep learning-based AES systems and also other state-of-the-art AES systems.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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