@InProceedings{hoang-haffari-cohn:2017:EMNLP2017,
  author    = {Hoang, Cong Duy Vu  and  Haffari, Gholamreza  and  Cohn, Trevor},
  title     = {Towards Decoding as Continuous Optimisation in 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     = {146--156},
  abstract  = {We propose a novel decoding approach for neural machine translation (NMT) based
	on continuous optimisation. We reformulate decoding, a discrete optimization
	problem, into a continuous problem, such that optimization can make use of
	efficient gradient-based techniques. Our powerful decoding framework allows for
	more accurate decoding for standard neural machine translation models, as well
	as enabling decoding in intractable models such as intersection of several
	different NMT models. Our empirical results show that our decoding framework is
	effective, and can leads to substantial improvements in translations,
	especially in situations where greedy search and beam search are not feasible.
	Finally, we show how the technique is highly competitive with, and
	complementary to, reranking.},
  url       = {https://www.aclweb.org/anthology/D17-1014}
}