@InProceedings{wang-EtAl:2017:I17-11,
  author    = {Wang, Yining  and  Zhao, Yang  and  Zhang, Jiajun  and  Zong, Chengqing  and  Xue, Zhengshan},
  title     = {Towards Neural Machine Translation with Partially Aligned Corpora},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {384--393},
  abstract  = {While neural machine translation (NMT) has become the new paradigm, the
	parameter optimization requires large-scale parallel data which is scarce in
	many domains and language pairs. In this paper, we address a new translation
	scenario in which there only exists monolingual corpora and phrase pairs. We
	propose a new method towards translation with partially aligned sentence pairs
	which are derived from the phrase pairs and monolingual corpora. To make full
	use of the partially aligned corpora, we adapt the conventional NMT training
	method in two aspects. On one hand, different generation strategies are
	designed for aligned and unaligned target words. On the other hand, a different
	objective function is designed to model the partially aligned parts. The
	experiments demonstrate that our method can achieve a relatively good result in
	such a translation scenario, and tiny bitexts can boost translation quality to
	a large extent.},
  url       = {http://www.aclweb.org/anthology/I17-1039}
}