English side of the corpus has been parsed by a statistical parser.

SIMT Motivation {#motivation}
===============

As in many of the frameworks described above, the aim of the SIMT effort was to provide a generalized framework for representing structured semantic information, such as modality and negation. Unlike many of the previous semantic annotation efforts (where the emphasis tends to be on English), however, our approach is designed to be directly integrated into a translation engine, with the goal of translating highly divergent language pairs, such as Urdu and English. As such, our choice of annotation scheme—illustrated in the trigger-target example shown in Figure \[modality-example\]—was based on a simplified structural representation that is general enough to accommodate divergent modality/negation phenomena, easy for language experts to follow, and straightforward to integrate into a tree-grafting mechanism for MT. Our objective is to investigate whether incorporating this sort of information into machine translation systems could produce better translations, particularly in settings where only small parallel corpora are available.

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  set          lines   tokens   types   tokens   types
  training      202k     1.7M     56k     1.7M     51k
  dev            981      21k      4k      19k      4k
  devtest        883      22k      4k   19-20k      4k
  test          1792      42k      6k   38-41k      5k
  