  Recent years have brought great advances into solving morphological tasks,
mostly due to powerful neural models applied to various tasks as (re)inflection
and analysis. Yet, such morphological tasks cannot be considered solved,
especially when little training data is available or when generalizing to
previously unseen lemmas. This work explores effects on performance obtained
through various ways in which morphological models get access to subcharacter
phonological features that are the targets of morphological processes. We
design two methods to achieve this goal: one that leaves models as is but
manipulates the data to include features instead of characters, and another
that manipulates models to take phonological features into account when
building representations for phonemes. We elicit phonemic data from standard
graphemic data using language-specific grammars for languages with shallow
grapheme-to-phoneme mapping, and we experiment with two reinflection models
over eight languages. Our results show that our methods yield comparable
results to the grapheme-based baseline overall, with minor improvements in some
of the languages. All in all, we conclude that patterns in character
distributions are likely to allow models to infer the underlying phonological
characteristics, even when phonemes are not explicitly represented.
