  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.
