@InProceedings{stahlberg-EtAl:2017:EACLshort,
  author    = {Stahlberg, Felix  and  de Gispert, Adri\`{a}  and  Hasler, Eva  and  Byrne, Bill},
  title     = {Neural Machine Translation by Minimising the Bayes-risk with Respect to Syntactic Translation Lattices},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {362--368},
  abstract  = {We present a novel scheme to combine neural machine translation (NMT) with
	traditional statistical machine translation (SMT). Our approach borrows ideas
	from linearised lattice minimum Bayes-risk decoding for SMT. The NMT score is
	combined with the Bayes-risk of the translation according the SMT lattice. This
	makes our approach much more flexible than n-best list or lattice rescoring
	as the neural decoder is not restricted to the SMT search space. We show an
	efficient and simple way to integrate risk estimation into the NMT decoder
	which is suitable for word-level as well as subword-unit-level NMT. We test our
	method on English-German and Japanese-English and report significant gains over
	lattice rescoring on several data sets for both single and ensembled NMT. The
	MBR decoder produces entirely new hypotheses far beyond simply rescoring the
	SMT search space or fixing UNKs in the NMT output.},
  url       = {http://www.aclweb.org/anthology/E17-2058}
}