To solve these problems, we propose a convolution enhanced bilingual recursive neural network (Con-vBRNN), which exploits word alignments to guide the generation of phrase structures and then integrates embeddings of different linguistic units on the phrase structures into bilingual semantic modeling.
we develop a new recursive neural network, in which the composition criterion for tree construction is the degree of consistency to word alignments rather than the reconstruction error.