@InProceedings{marcheggiani-titov:2017:EMNLP2017,
  author    = {Marcheggiani, Diego  and  Titov, Ivan},
  title     = {Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1506--1515},
  abstract  = {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.},
  url       = {https://www.aclweb.org/anthology/D17-1159}
}