We describe a method for prediction of linguistic
structure in a language for which only
unlabeled data is available, using annotated
data from a set of one or more helper languages.
Our approach is based on a model
that locally mixes between supervised models
from the helper languages. Parallel data
is not used, allowing the technique to be applied
even in domains where human-translated
texts are unavailable. We obtain state-of-theart
performance for two tasks of structure prediction:
unsupervised part-of-speech tagging
and unsupervised dependency parsing.