@InProceedings{wang-EtAl:2017:Short1,
  author    = {Wang, Weiyue  and  Alkhouli, Tamer  and  Zhu, Derui  and  Ney, Hermann},
  title     = {Hybrid Neural Network Alignment and Lexicon Model in Direct HMM for Statistical Machine Translation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {125--131},
  abstract  = {Recently, the neural machine translation systems showed their promising
	performance and surpassed the phrase-based systems for most translation tasks.
	Retreating into conventional concepts machine translation while utilizing
	effective neural models is vital for comprehending the leap accomplished by
	neural machine translation over phrase-based methods. This work proposes a
	direct HMM with neural network-based lexicon and alignment models, which are
	trained jointly using the Baum-Welch algorithm. The direct HMM is applied to
	rerank the n-best list created by a state-of-the-art phrase-based translation
	system and it provides improvements by up to 1.0% Bleu scores on two different
	translation tasks.},
  url       = {http://aclweb.org/anthology/P17-2020}
}