@InProceedings{hashimoto-tsuruoka:2017:EMNLP2017,
  author    = {Hashimoto, Kazuma  and  Tsuruoka, Yoshimasa},
  title     = {Neural Machine Translation with Source-Side Latent Graph Parsing},
  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     = {125--135},
  abstract  = {This paper presents a novel neural machine translation model which jointly
	learns translation and source-side latent graph representations of sentences.
	Unlike existing pipelined approaches using syntactic parsers, our end-to-end
	model learns a latent graph parser as part of the encoder of an attention-based
	neural machine translation model, and thus the parser is optimized according to
	the translation objective.
	In experiments, we first show that our model compares favorably with
	state-of-the-art sequential and pipelined syntax-based NMT models.
	We also show that the performance of our model can be further improved by
	pre-training it with a small amount of treebank annotations.
	Our final ensemble model significantly outperforms the previous best models on
	the standard English-to-Japanese translation dataset.},
  url       = {https://www.aclweb.org/anthology/D17-1012}
}