A single multilingual language model serializes all Wikidata edits into text and outperforms the production ORES system in vandalism detection and fairness.
Overview of the Wikidata Vandalism Detection Task at WSDM Cup 2017
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abstract
We report on the Wikidata vandalism detection task at the WSDM Cup 2017. The task received five submissions for which this paper describes their evaluation and a comparison to state of the art baselines. Unlike previous work, we recast Wikidata vandalism detection as an online learning problem, requiring participant software to predict vandalism in near real-time. The best-performing approach achieves a ROC-AUC of 0.947 at a PR-AUC of 0.458. In particular, this task was organized as a software submission task: to maximize reproducibility as well as to foster future research and development on this task, the participants were asked to submit their working software to the TIRA experimentation platform along with the source code for open source release.
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Graph-Linguistic Fusion: Using Language Models for Wikidata Vandalism Detection
A single multilingual language model serializes all Wikidata edits into text and outperforms the production ORES system in vandalism detection and fairness.