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Computer Science @ Rochester
Friday, November 02, 2007
10:00 AM
CSB 703
Hao Zhang
University of Rochester
Learning Minimum Translation Units with Synchronous Parsing
Multi-word translation pairs are the building blocks of machine translation systems. Most systems utilize a set of heuristics to extract such translation units from word-aligned bilingual corpora. We present a Bayesian learning approach within a structured space to learn basic multi-word translation units unsupervisedly. We show the effectiveness of Variational Bayes (VB) which biases towards sparser distributions of translation pairs. We also show that structural constraints that restrict the learning space to a subspace that is consistent with reliable single-word translation pairs is important and when combined with VB yields superior translation results.

This is a joint work with Chris Quirk at Microsoft Research at Redmond.