QCRI Machine Translation Systems for IWSLT 16

Summary

This paper describes QCRI's machine translation systems for the IWSLT 2016 evaluation campaign, focusing on Arabic-English and English-Arabic tracks. They built both Phrase-based and Neural machine translation models. A Neural MT system, trained by stacking data from different genres through fine-tuning, and applying ensemble over 8 models, outperformed a strong phrase-based system by 2 BLEU points in the Arabic->English direction. Why it matters: The research highlights the early promise of neural machine translation for Arabic language pairs, demonstrating its potential to surpass traditional methods.