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Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015

arXiv · Jun 18

This paper describes the QCRI-Columbia-NYUAD group's Egyptian Arabic-to-English statistical machine translation system submitted to the NIST OpenMT'2015 competition. The system used tools like 3arrib and MADAMIRA for processing and standardizing informal dialectal Arabic. The system was trained using phrase-based SMT with features such as operation sequence model, class-based language model and neural network joint model. Why it matters: The work demonstrates advances in machine translation for dialectal Arabic, a challenging but important area for regional communication and NLP research.

QCRI Machine Translation Systems for IWSLT 16

arXiv · Jan 14

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.