Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2009

Abstract

Web search quality can vary widely across languages, even for the same information need. We propose to exploit this variation in quality by learning a ranking function on bilingual queries: queries that appear in query logs for two languages but represent equivalent search interests. For a given bilingual query, along with corresponding monolingual query log and monolingual ranking, we generate a ranking on pairs of documents, one from each language. Then we learn a linear ranking function which exploits bilingual features on pairs of documents, as well as standard monolingual features. Finally, we show how to reconstruct monolingual ranking from a learned bilingual ranking. Using publicly available Chinese and English query logs, we demonstrate for both languages that our ranking technique exploiting bilingual data leads to significant improvements over a state-of-the-art monolingual ranking algorithm.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics (ACL 2019)

First Page

1075

Last Page

1083

Identifier

10.3115/1690219.1690296

Publisher

Association for Computational Linguistics

City or Country

Singapore

Additional URL

https://doi.org/10.3115/1690219.1690296

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