The heuristically trained baseline takes advantage of 'gap rules' to reorder based on lexical context cues, but makes very limited use of the hierarchical structure above the lexical surface.
we employ the induced probabilistic HR-SCFG G as the backbone of a log-linear, feature based translation model, with the derivation probability p(D) under the grammar estimate being one of the features.