framework for implementing semantically-informed machine translation. Our framework is not limited to the semantic annotations produced by the MN taggers that are the subject of this paper and we exploit this capability to additionally include named-entity annotations produced by a pre-existing tagger. By augmenting hierarchical phrase-based translation rules with syntactic labels that were extracted from a parsed parallel corpus, and further augmenting the parse trees with markers for modality, negation, and entities (through the tree grafting process), we produced a better model for translating Urdu and English. The resulting system significantly outperformed the linguistically naïve baseline Hiero model, and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task.

We note that while our largest gains were from syntactic enrichments to the model, smaller (but significant) gains were achieved by injecting semantic knowledge into the syntactic paradigm. Verbal semantics (modality and negation) contributed slightly more gains than nominal semantics (named entities) while their combined gains were the sum of their individual contributions.

Of course, the limited semantic types we explored (modality, negation and entities) are only a small piece of the much larger semantic space, but demonstrating success on these semantic aspects of language, the combination of 