  The manuscript presents an experiment at implementation of a Machine
Translation system in a MapReduce model. The empirical evaluation was done
using fully implemented translation systems embedded into the MapReduce
programming model. Two machine translation paradigms were studied: shallow
transfer Rule Based Machine Translation and Statistical Machine Translation.
  The results show that the MapReduce model can be successfully used to
increase the throughput of a machine translation system. Furthermore this
method enhances the throughput of a machine translation system without
decreasing the quality of the translation output.
  Thus, the present manuscript also represents a contribution to the seminal
work in natural language processing, specifically Machine Translation. It first
points toward the importance of the definition of the metric of throughput of
translation system and, second, the applicability of the machine translation
task to the MapReduce paradigm.
