  A common and effective way to train translation systems between related
languages is to consider sub-word level basic units. However, this increases
the length of the sentences resulting in increased decoding time. The increase
in length is also impacted by the specific choice of data format for
representing the sentences as subwords. In a phrase-based SMT framework, we
investigate different choices of decoder parameters as well as data format and
their impact on decoding time and translation accuracy. We suggest best options
for these settings that significantly improve decoding time with little impact
on the translation accuracy.
