  Although more additional corpora are now available for Statistical Machine
Translation (SMT), only the ones which belong to the same or similar domains
with the original corpus can indeed enhance SMT performance directly. Most of
the existing adaptation methods focus on sentence selection. In comparison,
phrase is a smaller and more fine grained unit for data selection, therefore we
propose a straightforward and efficient connecting phrase based adaptation
method, which is applied to both bilingual phrase pair and monolingual n-gram
adaptation. The proposed method is evaluated on IWSLT/NIST data sets, and the
results show that phrase based SMT performance are significantly improved (up
to +1.6 in comparison with phrase based SMT baseline system and +0.9 in
comparison with existing methods).
