  We describe a unified and coherent syntactic framework for supporting a
semantically-informed syntactic approach to statistical machine translation.
Semantically enriched syntactic tags assigned to the target-language training
texts improved translation quality. The resulting system significantly
outperformed a linguistically naive baseline model (Hiero), and reached the
highest scores yet reported on the NIST 2009 Urdu-English translation task.
This finding supports the hypothesis (posed by many researchers in the MT
community, e.g., in DARPA GALE) that both syntactic and semantic information
are critical for improving translation quality---and further demonstrates that
large gains can be achieved for low-resource languages with different word
order than English.
