Non-linear Learning for Statistical Machine Translation
Table 3: Comparison of different training criteria in number of new instances per iteration and training accuracy.
['Criteria', 'Pairs/iteration', 'Accuracy(%)']
Compared to BR which tries to separate the best hypothesis from the rest hypotheses in the n-best set, and PW which tries to obtain a correct ranking of all hypotheses, BW only aims at separating the best and worst hypothesis of each iteration, which is a easier task for learning a classifiers. It requires the least training instances and achieves the best performance in training. They are not calculated on the same set of instances, thus not directly comparable. We use the differences in accuracy as an indicator for the difficulties of the corresponding learning task.