  Lexical ambiguity, a challenging phenomenon in all natural languages, is
particularly prevalent for languages with diacritics that tend to be omitted in
writing, such as Arabic. Omitting diacritics leads to an increase in the number
of homographs: different words with the same spelling. Diacritic restoration
could theoretically help disambiguate these words, but in practice, the
increase in overall sparsity leads to performance degradation in NLP
applications. In this paper, we propose approaches for automatically marking a
subset of words for diacritic restoration, which leads to selective homograph
disambiguation. Compared to full or no diacritic restoration, these approaches
yield selectively-diacritized datasets that balance sparsity and lexical
disambiguation. We evaluate the various selection strategies extrinsically on
several downstream applications: neural machine translation, part-of-speech
tagging, and semantic textual similarity. Our experiments on Arabic show
promising results, where our devised strategies on selective diacritization
lead to a more balanced and consistent performance in downstream applications.
