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  : The size of the various data sets used for the experiments in this paper including the training, development (dev), incremental test set (devtest) and blind test set (test). The dev/devtest was a split of the NIST08 Urdu-English test set, and the blind test set was NIST09.[]{data-label="data-set-sizes"}

![An example of Urdu-English translation. Shown are an Urdu source document, a reference translation produced by a professional human translator, and machine translation output from a phrase-based model (Moses) without linguistic information, which is representative of state-of-the-art MT quality before the SIMT effort. []{data-label="Urdu-example"}](graphics/intro-example-revised-2){width="\linewidth"}

It is informative to look at an example translation to understand the challenges of translating important semantic elements when working with a low-resource language pair. Figure \[Urdu-example\] shows an example taken from the 2008 NIST Urdu-English translation task, and illustrates the translation quality of a state-of-the-art Urdu-English system (prior to the SIMT effort). The small amount of training data for this language pair (see Table \[data-set-sizes\]) results in significantly degraded translation quality compared, e.g., to an Arabic-English system that has more than 100 times the amount of training data.

The output in Figure \[Urdu-example\] was produced using Moses [@Moses], a state-of-the-art phrase-based MT 