# StRE: Self Attentive Edit Quality Prediction in Wikipedia

## TL;DR

This paper introduces StRE, a deep learning model that predicts Wikipedia edit quality using textual content and orthographic similarity, outperforming existing methods significantly.

## Contribution

The paper presents a novel deep encoder-based approach for edit quality prediction utilizing textual content, along with a large new dataset of revisions.

## Key findings

- StRE outperforms existing methods by 17% to 103%.
- Pretrained models achieve high accuracy with only 20% of edit data.
- First application of deep language models for Wikipedia content moderation.

## Abstract

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.