@InProceedings{yang-EtAl:2016:COLING,
  author    = {Yang, Zhen  and  Chen, Wei  and  Wang, Feng  and  Xu, Bo},
  title     = {A Character-Aware Encoder for Neural Machine Translation},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3063--3070},
  abstract  = {This article proposes a novel character-aware neural machine translation (NMT)
	model that views
	the input sequences as sequences of characters rather than words. On the use of
	row convolution
	(Amodei et al., 2015), the encoder of the proposed model composes word-level
	information from
	the input sequences of characters automatically. Since our model doesn’t rely
	on the boundaries
	between each word (as the whitespace boundaries in English), it is also applied
	to languages
	without explicit word segmentations (like Chinese). Experimental results on
	Chinese-English
	translation tasks show that the proposed character-aware NMT model can achieve
	comparable
	translation performance with the traditional word based NMT models. Despite the
	target side is
	still word based, the proposed model is able to generate much less unknown
	words.},
  url       = {http://aclweb.org/anthology/C16-1288}
}