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Computer Science @ Rochester
Friday, March 20, 2009
3:30 PM
CSB 703
Ding Liu
University of Rochester
Bayesian Learning of Phrasal Tree-to-String Templates
We examine the problem of overcoming noisy word-level alignments when learning tree-to-string translation rules. Our approach introduces new rules, and re-estimates rule probabilities using EM. The major obstacles to this approach are the very reasons that word-alignments are used for rule extraction: the huge space of possible rules, as well as controlling overfitting. By carefully controlling which portions of the original alignments are re-analyzed, and by using Bayesian inference during re-analysis, we show significant improvement over the baseline rules extracted from word-level alignments.