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.

本文介绍了QCRI针对IWSLT 2016的机器翻译系统。作者们建立了基于短语和神经网络的机器翻译模型，并比较了两种模型在阿拉伯语-英语语言对中性能的表现。研究结果表明，在阿拉伯语-英语翻译中，神经网络模型的BLEU值比基于短语的模型高2点。作者还将两种模型的输出进行了系统组合。

QCRI IWSLT 16 机器翻译系统