However, this would be quite slow: each iteration of the training procedure requires iterating through all n training sentences 6 once for each of the m candidate transformations, for a total cost of O(nm) where m grows (albeit sublinearly) with n.  Table 2. most useful transformations almost by definition are ones that are triggered the most frequently, any reasonably sized training set is likely to contain them, and so it is not actually likely that dramatically increasing the size of the training set will yield particularly large gains.
for this dataset, the initial state annotations (parse trees) were manually created by trained human annotators, as were the word alignments used to compute the agreement   score.