Crosslingual Section Title Alignment in Wikipedia.

Sections are the building blocks of Wikipedia articles. They are used by editors to create a structure for the content of articles, which in turn improves reading and editing workflows. Today, millions of carefully curated section titles exist in more than 160 actively edited Wikipedia languages as standalone components of a larger system. Understanding the connection and correspondence of section titles across languages presents various application opportunities such as article template recommendation, i.e., given a source language article, we can generate a skeleton of section titles for a target language. Inspired by this real-world data mining problem, the present paper introduces the problem of aligning section titles across Wikipedia languages and proposes a probabilistic method for identifying such correspondences. Instead of applying translation tools to section titles (which may generate out-of lexicon titles), we develop a supervised model that identifies cross-language mappings based on section content features. We collected a ground-truth dataset created for this purpose with the help of volunteers. In addition, we use Probabilistic Soft Logic to model the dependencies between multilingual section pairings. We show that our approach performs better than machine translation solutions in about 80% of the language pairs, including distant language mappings such as Arabic to Russian or French to Japanese and in many of the more closely related languages such as French to Spanish.