Even though most semantic-role formalisms are built upon constituent syntax, and only syntactic constituents can be labeled as arguments (e.g., FrameNet and PropBank), all the recent work on syntaxaware SRL relies on dependency representations of syntax.
we show how graph convolutional networks (GCNs) can be used to encode constituent structures and inform an SRL system.