  We describe a transfer method based on annotation projection to develop a
dependency-based semantic role labeling system for languages for which no
supervised linguistic information other than parallel data is available. Unlike
previous work that presumes the availability of supervised features such as
lemmas, part-of-speech tags, and dependency parse trees, we only make use of
word and character features. Our deep model considers using character-based
representations as well as unsupervised stem embeddings to alleviate the need
for supervised features. Our experiments outperform a state-of-the-art method
that uses supervised lexico-syntactic features on 6 out of 7 languages in the
Universal Proposition Bank.
