  Neural networks have become the state-of-the-art approach for machine
translation (MT) in many languages. While linguistically-motivated tokenization
techniques were shown to have significant effects on the performance of
statistical MT, it remains unclear if those techniques are well suited for
neural MT. In this paper, we systematically compare neural and statistical MT
models for Arabic-English translation on data preprecossed by various prominent
tokenization schemes. Furthermore, we consider a range of data and vocabulary
sizes and compare their effect on both approaches. Our empirical results show
that the best choice of tokenization scheme is largely based on the type of
model and the size of data. We also show that we can gain significant
improvements using a system selection that combines the output from neural and
statistical MT.
