Abstract
Diacritic restoration is the task of assigning diacritics (accents) for each character in a given segment. The typical input levels that have been previously used in diacritic restoration models are word and/or character units. In this paper, we investigate the use of subwords as input units along with their diacritic patterns (combinations of adjacent diacritics) as output, as an alternative to word or character-based models. Our experiments show that characters provide the optimal level of information for sequence-based diacritic restoration models across different languages. We additionally improved our diacritic restoration model by maximizing over the output diacritic sequence using a Conditional Random Field (CRF). Adding a CRF layer improves the performance on observed and unobserved words substantially for Arabic and marginally for Yoruba.
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