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We introduce a new syntax-aware model for dependency-based semantic rolelabeling that outperforms syntax-agnostic models for English and Spanish. Weuse 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 deephighway BiLSTM for semantic role labeling. Our model combines the strengths ofearlier models that performed SRL on the basis of a full dependency parse withmore recent models that use no syntactic information at all. Our local andnon-ensemble model achieves state-of-the-art performance on the CoNLL 09English and Spanish datasets. SRL models benefit from syntactic information,and we show that supertagging is a simple, powerful, and robust way toincorporate syntax into a neural SRL system.

Syntax-aware Neural Semantic Role Labeling with Supertags