Private access to phrase tables for statistical machine translation

Authors:
Nicola Cancedda
Affiliations:
Xerox Research Centre Europe, Meylan, France
Venue:
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Year:
2012

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Private information retrieval

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Abstract

Some Statistical Machine Translation systems never see the light because the owner of the appropriate training data cannot release them, and the potential user of the system cannot disclose what should be translated. We propose a simple and practical encryption-based method addressing this barrier.