@InProceedings{watanabe-tamura-ninomiya:2017:I17-2,
  author    = {Watanabe, Taiki  and  Tamura, Akihiro  and  Ninomiya, Takashi},
  title     = {CKY-based Convolutional Attention for Neural Machine Translation},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {1--6},
  abstract  = {This paper proposes a new attention
	mechanism for neural machine translation
	(NMT) based on convolutional neural
	networks (CNNs), which is inspired
	by the CKY algorithm. The proposed attention
	represents every possible combination
	of source words (e.g., phrases and
	structures) through CNNs, which imitates
	the CKY table in the algorithm. NMT,
	incorporating the proposed attention, decodes
	a target sentence on the basis of
	the attention scores of the hidden states
	of CNNs. The proposed attention enables
	NMT to capture alignments from underlying
	structures of a source sentence
	without sentence parsing. The evaluations
	on the Asian Scientific Paper Excerpt
	Corpus (ASPEC) English-Japanese translation
	task show that the proposed attention
	gains 0.66 points in BLEU.},
  url       = {http://www.aclweb.org/anthology/I17-2001}
}