Learning Non-Isomorphic Tree Mappings for Machine Translation

@inproceedings{Eisner2003LearningNT,
  title={Learning Non-Isomorphic Tree Mappings for Machine Translation},
  author={Jason Eisner},
  booktitle={ACL},
  year={2003}
}
Often one may wish to learn a tree-to-tree mapping, training it on unaligned pairs of trees, or on a mixture of trees and strings. Unlike previous statistical formalisms (limited to isomorphic trees),synchronous TSGallows local distortion of the tree topology. We reformulate it to permit dependency trees, and sketch EM/Viterbi algorithms for alignment, training, and decoding. 
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