@InProceedings{calixto-liu-campbell:2017:Long,
  author    = {Calixto, Iacer  and  Liu, Qun  and  Campbell, Nick},
  title     = {Doubly-Attentive Decoder for Multi-modal Neural Machine Translation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1913--1924},
  abstract  = {We introduce a Multi-modal Neural Machine Translation model in which a
	doubly-attentive decoder naturally incorporates spatial visual features
	obtained using pre-trained convolutional neural networks, bridging the gap
	between image description and translation. Our decoder learns to attend to
	source-language words and parts of an image independently by means of two
	separate attention mechanisms as it generates words in the target language. We
	find that our model can efficiently exploit not just back-translated in-domain
	multi-modal data but also large general-domain text-only MT corpora. We also
	report state-of-the-art results on the Multi30k data set.},
  url       = {http://aclweb.org/anthology/P17-1175}
}