  By investigating the distribution of phrase pairs in phrase translation
tables, the work in this paper describes an approach to increase the number of
n-gram alignments in phrase translation tables output by a sampling-based
alignment method. This approach consists in enforcing the alignment of n-grams
in distinct translation subtables so as to increase the number of n-grams.
Standard normal distribution is used to allot alignment time among translation
subtables, which results in adjustment of the distribution of n- grams. This
leads to better evaluation results on statistical machine translation tasks
than the original sampling-based alignment approach. Furthermore, the
translation quality obtained by merging phrase translation tables computed from
the sampling-based alignment method and from MGIZA++ is examined.
