We propose a novel multitask learning method for diacritization which trains a model to both diacritize and translate. Our method addresses data sparsity by exploiting large, readily avail able bitext corpora. Furthermore, transla tion requires implicit linguistic and seman tic knowledge, which is helpful for resolving ambiguities in diacritization. We apply our method to the Penn Arabic Treebank and re port a new stateoftheart word error rate of 4.79%. We also conduct manual and automatic analysis to better understand our method and highlight some of the remaining challenges in diacritization. Our method has applications in texttospeech, speechtospeech translation, and other NLP tasks. * Work done while at Apple. 1 Notable exceptions include the Quran and many chil dren's books.