  Character-level models have become a popular approach specially for their
accessibility and ability to handle unseen data. However, little is known on
their ability to reveal the underlying morphological structure of a word, which
is a crucial skill for high-level semantic analysis tasks, such as semantic
role labeling (SRL). In this work, we train various types of SRL models that
use word, character and morphology level information and analyze how
performance of characters compare to words and morphology for several
languages. We conduct an in-depth error analysis for each morphological
typology and analyze the strengths and limitations of character-level models
that relate to out-of-domain data, training data size, long range dependencies
and model complexity. Our exhaustive analyses shed light on important
characteristics of character-level models and their semantic capability.
