  We introduce a new syntax-aware model for dependency-based semantic role
labeling that outperforms syntax-agnostic models for English and Spanish. We
use a BiLSTM to tag the text with supertags extracted from dependency parses,
and we feed these supertags, along with words and parts of speech, into a deep
highway BiLSTM for semantic role labeling. Our model combines the strengths of
earlier models that performed SRL on the basis of a full dependency parse with
more recent models that use no syntactic information at all. Our local and
non-ensemble model achieves state-of-the-art performance on the CoNLL 09
English and Spanish datasets. SRL models benefit from syntactic information,
and we show that supertagging is a simple, powerful, and robust way to
incorporate syntax into a neural SRL system.
