@InProceedings{bastings-EtAl:2017:EMNLP2017,
  author    = {Bastings, Joost  and  Titov, Ivan  and  Aziz, Wilker  and  Marcheggiani, Diego  and  Simaan, Khalil},
  title     = {Graph Convolutional Encoders for Syntax-aware Neural Machine Translation},
  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     = {1957--1967},
  abstract  = {We present a simple and effective approach to incorporating syntactic structure
	into neural attention-based encoder-decoder models for machine translation. We
	rely on graph-convolutional networks (GCNs), a recent class of neural networks
	developed for modeling graph-structured data. Our GCNs use predicted syntactic
	dependency trees of source sentences to produce representations of words (i.e.
	hidden states of the encoder) that are sensitive to their syntactic
	neighborhoods. GCNs take word representations as input and produce word
	representations as output, so they can easily be incorporated as layers into
	standard encoders (e.g., on top of bidirectional RNNs or convolutional neural
	networks). We evaluate their effectiveness with English-German and
	English-Czech translation experiments for different types of encoders and
	observe substantial improvements over their syntax-agnostic versions in all the
	considered setups.},
  url       = {https://www.aclweb.org/anthology/D17-1209}
}