@InProceedings{feng-EtAl:2016:COLING2,
  author    = {Feng, Xiaocheng  and  Tang, Duyu  and  Qin, Bing  and  Liu, Ting},
  title     = {English-Chinese Knowledge Base Translation with Neural Network},
  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     = {2935--2944},
  abstract  = {Knowledge base (KB) such as Freebase plays an important role for many natural
	language processing tasks.
	English knowledge base is obviously larger and of higher quality than low
	resource language like Chinese.
	To expand Chinese KB by leveraging English KB resources, an effective way is to
	translate English KB (source) into Chinese (target).
	In this direction, two major challenges are to model triple semantics and to
	build a robust KB translator.
	We address these challenges by presenting a neural network approach, which
	learns continuous triple representation with a gated neural network. 
	Accordingly, source triples and target triples are mapped in the same semantic
	vector space.
	We build a new dataset for English-Chinese KB translation from Freebase, and
	compare with several baselines on it.
	Experimental results show that the proposed method improves translation
	accuracy compared with baseline methods. 
	We show that adaptive composition model improves standard solution such as
	neural tensor network in terms of translation accuracy.},
  url       = {http://aclweb.org/anthology/C16-1276}
}