  When parsing morphologically-rich languages with neural models, it is
beneficial to model input at the character level, and it has been claimed that
this is because character-level models learn morphology. We test these claims
by comparing character-level models to an oracle with access to explicit
morphological analysis on twelve languages with varying morphological
typologies. Our results highlight many strengths of character-level models, but
also show that they are poor at disambiguating some words, particularly in the
face of case syncretism. We then demonstrate that explicitly modeling
morphological case improves our best model, showing that character-level models
can benefit from targeted forms of explicit morphological modeling.
