论文标题
NWQM：Wikipedia的神经质量评估框架
NwQM: A neural quality assessment framework for Wikipedia
论文作者
论文摘要
不论社会经济和人口背景如何，数以百万计的人每天都取决于Wikipedia文章，以使自己了解流行和晦涩的话题。文章已由编辑分类为几个质量类别，这表明其可靠性是百科全书内容。此手动指定是一项繁重的任务，因为它需要有关百科全书的深刻知识，并且还需要导航Wiki指南的巡回赛。在本文中，我们提出了神经wikipedia QualtyMonitor（NWQM），这是一个新颖的深度学习模型，该模型从几种关键信息来源（例如文章文本，元数据和图像）中积累信号，以获得改进的Wikipedia文章表示。我们将方法与多种可用解决方案进行了比较，并通过详细的消融研究显示了对最先进方法的8％改善。
Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia QualityMonitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8% improvement over state-of-the-art approaches with detailed ablation studies.
