R-VGAE：用于无监督前提链学习的矩阵-变分图自编码器
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
摘要：
概念前提链学习的任务是自动判断概念对之间是否存在前提关系。在本文中，我们将概念之间的学习先决条件关系框架为无监督任务，在训练过程中无法访问标记的概念对。我们提出了一个模型，称为矩阵-变分图自动编码器（R-VGAE），以预测概念和资源节点组成的图中的概念关系。结果表明，我们的无监督方法优于基于图的半监督方法和其他基线方法高达9.77%和10.47%的先决条件关系预测精度和F1得分。值得注意的是，我们的方法是第一个基于图的模型，它试图利用深度学习表示来完成无监督的先决条件学习任务。我们还扩展了现有的语料库，其中共有1,717个英语自然语言处理（NLP）相关的讲座幻灯片文件和手动概念对注释超过322个主题。
英文摘要：
The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs. In this paper, we frame learning prerequisite relationships among concepts as an unsupervised task with no access to labeled concept pairs during training. We propose a model called the Relational-Variational Graph AutoEncoder (R-VGAE) to predict concept relations within a graph consisting of concept and resource nodes. Results show that our unsupervised approach outperforms graph-based semi-supervised methods and other baseline methods by up to 9.77% and 10.47% in terms of prerequisite relation prediction accuracy and F1 score. Our method is notably the first graph-based model that attempts to make use of deep learning representations for the task of unsupervised prerequisite learning. We also expand an existing corpus which totals 1,717 English Natural Language Processing (NLP)-related lecture slide files and manual concept pair annotations over 322 topics.
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。
