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Neural approaches to learning term embeddings have led to improvedcomputation of similarity and ranking in information retrieval (IR). So farneural representation learning has not been extended to meta-textualinformation that is readily available for many IR tasks, for example, patentclasses in prior-art retrieval, topical information in Wikipedia articles, orproduct categories in e-commerce data. We present a framework that learnsembeddings for meta-textual categories, and optimizes a pairwise rankingobjective for improved matching based on combined embeddings of textual andmeta-textual information. We show considerable gains in an experimentalevaluation on cross-lingual retrieval in the Wikipedia domain for threelanguage pairs, and in the Patent domain for one language pair. Our resultsemphasize that the mode of combining different types of information is crucialfor model improvement.

Embedding Meta-Textual Information for Improved Learning to Rank