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 propose to use recursive autoencoders to make full use of the entire merging blocks alternatively. The recursive autoencoders are capable of generating vector space representations for variable-sized phrases, which enable predicting orders to exploit syntactic and semantic information from a neural language modeling's perspective. Experiments on the NIST 2008 dataset show that our system significantly improves over the MaxEnt classifier by 1.07 BLEU points.
REF presented an ITG reordering classifier based on recursive autoencoders, and generated vector space representations for variable-sized phrases, which enable predicting orders to exploit syntactic and semantic information.
Recursive Autoencoders for ITG-Based Translation
EMNLP
Computer Science