@InProceedings{su-EtAl:2016:COLING,
  author    = {su, jinsong  and  Zhang, Biao  and  Xiong, Deyi  and  Li, Ruochen  and  Yin, Jianmin},
  title     = {Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling},
  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     = {3071--3081},
  abstract  = {Estimating similarities at different levels of linguistic units, such as words,
	sub-phrases and
	phrases, is helpful for measuring semantic similarity of an entire bilingual
	phrase. In this paper,
	we propose a convolution-enhanced bilingual recursive neural network
	(ConvBRNN), which not
	only exploits word alignments to guide the generation of phrase structures but
	also integrates
	multiple-level information of the generated phrase structures into bilingual
	semantic modeling.
	In order to accurately learn the semantic hierarchy of a bilingual phrase, we
	develop a recursive
	neural network to constrain the learned bilingual phrase structures to be
	consistent with word
	alignments. Upon the generated source and target phrase structures, we stack a
	convolutional
	neural network to integrate vector representations of linguistic units on the
	structures into bilingual
	phrase embeddings. After that, we fully incorporate information of different
	linguistic units
	into a bilinear semantic similarity model. We introduce two max-margin losses
	to train the ConvBRNN
	model: one for the phrase structure inference and the other for the semantic
	similarity
	model. Experiments on NIST Chinese-English translation tasks demonstrate the
	high quality of
	the generated bilingual phrase structures with respect to word alignments and
	the effectiveness
	of learned semantic similarities on machine translation.},
  url       = {http://aclweb.org/anthology/C16-1289}
}