  We present the second ever evaluated Arabic dialect-to-dialect machine
translation effort, and the first to leverage external resources beyond a small
parallel corpus. The subject has not previously received serious attention due
to lack of naturally occurring parallel data; yet its importance is evidenced
by dialectal Arabic's wide usage and breadth of inter-dialect variation,
comparable to that of Romance languages. Our results suggest that modeling
morphology and syntax significantly improves dialect-to-dialect translation,
though optimizing such data-sparse models requires consideration of the
linguistic differences between dialects and the nature of available data and
resources. On a single-reference blind test set where untranslated input scores
6.5 BLEU and a model trained only on parallel data reaches 14.6, pivot
techniques and morphosyntactic modeling significantly improve performance to
17.5.
