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Private Access to Phrase Tables for Statistical Machine Translation

Nicola Cancedda
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.
The 50th Annual Meeting of the Association for Computational Linguistics, Jeju, Republic of Korea, 8-14 July, 2012.
2012
2012/031