@InProceedings{gu-EtAl:2017:EACLlong,
  author    = {Gu, Jiatao  and  Neubig, Graham  and  Cho, Kyunghyun  and  Li, Victor O.K.},
  title     = {Learning to Translate in Real-time with Neural Machine Translation},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {1053--1062},
  abstract  = {Translating in real-time, a.k.a.simultaneous translation, outputs translation
	words before the input sentence ends,
	which is a challenging problem for conventional machine translation methods. 
	We propose a neural machine translation (NMT) framework for simultaneous
	translation in which an agent learns to make decisions on when to translate
	from the interaction with a pre-trained NMT environment.
	To trade off quality and delay, we extensively explore various targets for
	delay and design a method for beam-search applicable in the simultaneous MT
	setting. Experiments against state-of-the-art baselines on two language pairs
	demonstrate the efficacy of the proposed framework both quantitatively and
	qualitatively.},
  url       = {http://www.aclweb.org/anthology/E17-1099}
}