  Our paper addresses the problem of annotation projection for semantic role
labeling for resource-poor languages using supervised annotations from a
resource-rich language through parallel data. We propose a transfer method that
employs information from source and target syntactic dependencies as well as
word alignment density to improve the quality of an iterative bootstrapping
method. Our experiments yield a $3.5$ absolute labeled F-score improvement over
a standard annotation projection method.
