  Semantic role labeling (SRL) is the task of identifying the
predicate-argument structure of a sentence. It is typically regarded as an
important step in the standard NLP pipeline. As the semantic representations
are closely related to syntactic ones, we exploit syntactic information in our
model. We propose a version of graph convolutional networks (GCNs), a recent
class of neural networks operating on graphs, suited to model syntactic
dependency graphs. GCNs over syntactic dependency trees are used as sentence
encoders, producing latent feature representations of words in a sentence. We
observe that GCN layers are complementary to LSTM ones: when we stack both GCN
and LSTM layers, we obtain a substantial improvement over an already
state-of-the-art LSTM SRL model, resulting in the best reported scores on the
standard benchmark (CoNLL-2009) both for Chinese and English.
