which has been unexplored by the statistical machine translation community, bodes well for (larger) improvements based on the incorporation of other semantic aspects (e.g., relations and temporal knowledge). Moreover, we believe this syntactic framework to be well suited for further exploration of the impact of many different types of semantics on the quality of machine-translation (MT) output. Indeed, it would not have been possible to initiate the current study without the foundational work that gave rise to a syntactic paradigm that could support these semantic enrichments.

In the SIMT paradigm, semantic elements (e.g., modality/negation) are identified in the English portion of a parallel training corpus and projected to the source language (in our case, Urdu) during a process of syntactic alignment. These semantic elements are subsequently used in the translation rules that are extracted from the parallel corpus. The goal of adding them to the translation rules is to constrain the space of possible translations to more grammatical and more semantically coherent output. We explored whether including such semantic elements could improve translation output in the face of sparse training data and few source language annotations. Results were encouraging. Translation quality, as measured by the Bleu metric [@Papineni:2002], improved when 