  The lack of parallel data for many language pairs is an important challenge
to statistical machine translation (SMT). One common solution is to pivot
through a third language for which there exist parallel corpora with the source
and target languages. Although pivoting is a robust technique, it introduces
some low quality translations especially when a poor morphology language is
used as the pivot between rich morphology languages. In this paper, we examine
the use of synchronous morphology constraint features to improve the quality of
phrase pivot SMT. We compare hand-crafted constraints to those learned from
limited parallel data between source and target languages. The learned
morphology constraints are based on projected align- ments between the source
and target phrases in the pivot phrase table. We show positive results on
Hebrew-Arabic SMT (pivoting on English). We get 1.5 BLEU points over a phrase
pivot baseline and 0.8 BLEU points over a system combination baseline with a
direct model built from parallel data.
