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The rapid globalization of Wikipedia is generating a parallel, multi-lingual corpus of unprecedented scale. Pages for the same topic in many different languages emerge both as a result of manual translation and independent development. Unfortunately, these pages may appear at different times, vary in size, scope, and quality. Furthermore, differential growth rates cause the conceptual mapping between articles in different languages to be both complex and dynamic. These disparities provide the opportunity for a powerful form of information arbitrage--leveraging articles in one or more languages to improve the content in another. Analyzing four large language domains (English, Spanish, French, and German), we present Ziggurat, an automated system for aligning Wikipedia infoboxes, creating new infoboxes as necessary, filling in missing information, and detecting discrepancies between parallel pages. Our method uses self-supervised learning and our experiments demonstrate the method's feasibility, even in the absence of dictionaries.
Adar et al. (Mon,) studied this question.