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An Infinite Hierarchical Bayesian Model of Phrasal Translation

Trevor Cohn and Gholamreza Haffari

The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 2013


Modern phrase-based machine translation systems make extensive use of word-based translation models for inducing alignments from parallel corpora. This is problematic, as the systems are incapable of accurately modelling many translation phenomena that do not decompose into word-for-word translation. This paper presents a novel method for inducing phrase-based translation units directly from parallel data, which we frame as learning an inverse transduction grammar (ITG) using a recursive Bayesian prior. Overall this leads to a model which learns translations of entire sentences, while also learning their decomposition into smaller units (phrase-pairs) recursively, terminating at word translations. Our experiments on Arabic, Urdu and Farsi to English demonstrate improvements over competitive baseline systems.

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