@InProceedings{wang-EtAl:2017:EMNLP20173,
  author    = {Wang, Xing  and  Tu, Zhaopeng  and  Xiong, Deyi  and  Zhang, Min},
  title     = {Translating Phrases in Neural Machine Translation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1421--1431},
  abstract  = {Phrases play an important role in natural language understanding and machine
	translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is
	difficult to integrate them into current neural machine translation (NMT) which
	reads and generates sentences word by word. In this work, we propose a method
	to translate phrases in NMT by integrating a phrase memory storing target
	phrases from a phrase-based statistical machine translation (SMT) system into
	the encoder-decoder architecture of NMT. At each decoding step, the phrase
	memory is first re-written by the SMT model, which dynamically generates
	relevant target phrases with contextual information provided by the NMT model.
	Then the proposed model reads the phrase memory to make probability estimations
	for all phrases in the phrase memory. If phrase generation is carried on, the
	NMT decoder selects an appropriate phrase from the memory to perform phrase
	translation and updates its decoding state by consuming the words in the
	selected phrase. Otherwise, the NMT decoder generates a word from the
	vocabulary as the general NMT decoder does. Experiment results on the Chinese
	to
	English translation show that the proposed model achieves significant
	improvements over the baseline on various test sets.},
  url       = {https://www.aclweb.org/anthology/D17-1149}
}