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While inversion transduction grammar (ITG) is well suited for modeling ordering shifts between languages, how to make applying the two reordering rules (i.e., straight and inverted) dependent on actual blocks being merged remains a challenge. Unlike previous work that only uses boundary words, we pro-pose to use recursive autoencoders to make full use of the entire merging blocks alter-natively. The recursive autoencoders are ca-pable of generating vector space representa-tions for variable-sized phrases, which enable predicting orders to exploit syntactic and se-mantic information from a neural language modeling’s perspective. Experiments on the NIST 2008 dataset show that our system sig-nificantly improves over the MaxEnt classifier by 1.07 BLEU points. 1
Li et al. (Tue,) studied this question.