  Wikipedia can easily be justified as a behemoth, considering the sheer volume
of content that is added or removed every minute to its several projects. This
creates an immense scope, in the field of natural language processing towards
developing automated tools for content moderation and review. In this paper we
propose Self Attentive Revision Encoder (StRE) which leverages orthographic
similarity of lexical units toward predicting the quality of new edits. In
contrast to existing propositions which primarily employ features like page
reputation, editor activity or rule based heuristics, we utilize the textual
content of the edits which, we believe contains superior signatures of their
quality. More specifically, we deploy deep encoders to generate representations
of the edits from its text content, which we then leverage to infer quality. We
further contribute a novel dataset containing 21M revisions across 32K
Wikipedia pages and demonstrate that StRE outperforms existing methods by a
significant margin at least 17% and at most 103%. Our pretrained model achieves
such result after retraining on a set as small as 20% of the edits in a
wikipage. This, to the best of our knowledge, is also the first attempt towards
employing deep language models to the enormous domain of automated content
moderation and review in Wikipedia.
