@InProceedings{liu-EtAl:2016:COLING,
  author    = {Liu, Lemao  and  Utiyama, Masao  and  Finch, Andrew  and  Sumita, Eiichiro},
  title     = {Neural Machine Translation with Supervised Attention},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3093--3102},
  abstract  = {The attention mechanism is appealing for neural machine translation,
	since it is able to dynamically encode a source sentence by generating a
	alignment between a target word and source words. Unfortunately, it has been
	proved to be worse than conventional alignment models in alignment accuracy. In
	this paper, we analyze and explain this issue from the point view of
	reordering, and propose a supervised attention which is learned with guidance
	from conventional alignment models. Experiments on two Chinese-to-English
	translation tasks show that the supervised attention mechanism yields better
	alignments leading to substantial gains over the standard attention based NMT.},
  url       = {http://aclweb.org/anthology/C16-1291}
}