  Semantic role labeling (SRL) is the task of identifying predicates and
labeling argument spans with semantic roles. 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 syntax-aware SRL relies on dependency representations of syntax. In
contrast, we show how graph convolutional networks (GCNs) can be used to encode
constituent structures and inform an SRL system. Nodes in our SpanGCN
correspond to constituents. The computation is done in 3 stages. First, initial
node representations are produced by `composing' word representations of the
first and the last word in the constituent. Second, graph convolutions relying
on the constituent tree are performed, yielding syntactically-informed
constituent representations. Finally, the constituent representations are
`decomposed' back into word representations which in turn are used as input to
the SRL classifier. We evaluate SpanGCN against alternatives, including a model
using GCNs over dependency trees, and show its effectiveness on standard
CoNLL-2005, CoNLL-2012, and FrameNet benchmarks.
