  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 available bitext corpora. Furthermore, translation
requires implicit linguistic and semantic knowledge, which is helpful for
resolving ambiguities in the diacritization task. We apply our method to the
Penn Arabic Treebank and report a new state-of-the-art 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.
