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Machine translation between Arabic and Hebrew has so far been limited by alack of parallel corpora, despite the political and cultural importance of thislanguage pair. Previous work relied on manually-crafted grammars or pivotingvia English, both of which are unsatisfactory for building a scalable andaccurate MT system. In this work, we compare standard phrase-based and neuralsystems on Arabic-Hebrew translation. We experiment with tokenization byexternal tools and sub-word modeling by character-level neural models, and showthat both methods lead to improved translation performance, with a smalladvantage to the neural models.

Large-Scale Machine Translation between Arabic and Hebrew: Available Corpora and Initial Results