  Machine translation between Arabic and Hebrew has so far been limited by a
lack of parallel corpora, despite the political and cultural importance of this
language pair. Previous work relied on manually-crafted grammars or pivoting
via English, both of which are unsatisfactory for building a scalable and
accurate MT system. In this work, we compare standard phrase-based and neural
systems on Arabic-Hebrew translation. We experiment with tokenization by
external tools and sub-word modeling by character-level neural models, and show
that both methods lead to improved translation performance, with a small
advantage to the neural models.
