  In many languages like Arabic, diacritics are used to specify pronunciations
as well as meanings. Such diacritics are often omitted in written text,
increasing the number of possible pronunciations and meanings for a word. This
results in a more ambiguous text making computational processing on such text
more difficult. Diacritic restoration is the task of restoring missing
diacritics in the written text. Most state-of-the-art diacritic restoration
models are built on character level information which helps generalize the
model to unseen data, but presumably lose useful information at the word level.
Thus, to compensate for this loss, we investigate the use of multi-task
learning to jointly optimize diacritic restoration with related NLP problems
namely word segmentation, part-of-speech tagging, and syntactic diacritization.
We use Arabic as a case study since it has sufficient data resources for tasks
that we consider in our joint modeling. Our joint models significantly
outperform the baselines and are comparable to the state-of-the-art models that
are more complex relying on morphological analyzers and/or a lot more data
(e.g. dialectal data).
