We compare three methods of modeling morphological features in statistical machine translation (SMT) from English to Arabic, a morphologically rich language. Features can be modeled as part of the core translation process mapping source tokens to target tokens. Alternatively these features can be generated using target monolingual context as part of a separate generation (or post-translation in...