Given the amount of variability between the outputs of different parsers and word aligners (or even the same systems with different settings), the best way to improve agreement is to learn a transformation sequence that is specifically tuned for the same annotators (parsers and word aligners) we are evaluating with.
we found that though training on the English Chinese Translation Treebank produces clean, interpretable rules, preliminary experiments showed little to no improvement from using these rules for MT, primarily because actual alignments are not only noisier but also systematically different from gold ones.