  This paper describes QCRI's machine translation systems for the IWSLT 2016
evaluation campaign. We participated in the Arabic->English and English->Arabic
tracks. We built both Phrase-based and Neural machine translation models, in an
effort to probe whether the newly emerged NMT framework surpasses the
traditional phrase-based systems in Arabic-English language pairs. We trained a
very strong phrase-based system including, a big language model, the Operation
Sequence Model, Neural Network Joint Model and Class-based models along with
different domain adaptation techniques such as MML filtering, mixture modeling
and using fine tuning over NNJM model. However, a Neural MT system, trained by
stacking data from different genres through fine-tuning, and applying ensemble
over 8 models, beat our very strong phrase-based system by a significant 2 BLEU
points margin in Arabic->English direction. We did not obtain similar gains in
the other direction but were still able to outperform the phrase-based system.
We also applied system combination on phrase-based and NMT outputs.
