论文标题
R-VGAE：无监督先决条件链学习的关系变化图形自动编码器
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
论文作者
论文摘要
概念的任务先决条件的链学习是自动确定概念对之间的先决关系的存在。在本文中，我们将学习概念之间的先决关系作为无监督的任务，而在培训过程中无需访问标记的概念对。我们提出了一个称为关系变化图自动编码器（R-VGAE）的模型，以预测由概念和资源节点组成的图中的概念关系。结果表明，就先决条件的关系预测准确性和F1得分而言，我们的无监督方法优于基于图形的半监督方法和其他基线方法的最高9.77％和10.47％。我们的方法是第一个基于图的模型，该模型试图利用深度学习表示，以实现无监督的先决条件学习的任务。我们还扩展了一个现有的语料库，该语料库总计1,717个英语自然语言处理（NLP）相关的讲座文件和手动概念对对322个主题的注释。
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.
