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We propose a novel multitask learning method for diacritization which trainsa model to both diacritize and translate. Our method addresses data sparsity byexploiting large, readily available bitext corpora. Furthermore, translationrequires implicit linguistic and semantic knowledge, which is helpful forresolving ambiguities in the diacritization task. We apply our method to thePenn Arabic Treebank and report a new state-of-the-art word error rate of4.79%. We also conduct manual and automatic analysis to better understand ourmethod and highlight some of the remaining challenges in diacritization.

Improving Arabic Diacritization by Learning to Diacritize and Translate