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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 reanalyzed, and by using Bayesian inference during re-analysis, we show significant improvement over the baseline rules extracted from word-level alignments.

Bayesian learning of phrasal tree-to-string templates