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In this paper, we describe the CMUQ system we submitted to The ANLP-QALB 2014 Shared Task on Automatic Text Correction for Arabic. Our system combines rule-based linguistic techniques with statistical language modeling techniques and machine translation-based methods. Our system outperforms the baseline and reaches an F-score of 65.42% on the test set of(More)
In this paper, we present a statistical machine translation system for English to Di-alectal Arabic (DA), using Modern Standard Arabic (MSA) as a pivot. We create a core system to translate from En-glish to MSA using a large bilingual parallel corpus. Then, we design two separate pathways for translation from MSA into DA: a two-step domain and dialect(More)
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