We present a framework for cross-lingual transfer of sequence information from a
resource-rich source language to a resource-impoverished target language that incorporates
soft constraints via posterior regularization. To this end, we use automatically word
aligned bitext between the source and target language pair, and learn a discriminative conditional random field model
on the target side. Our posterior regularization constraints are derived from simple intuitions
about the task at hand and from cross-lingual alignment information.
We show improvements over strong baselines for
two tasks: part-of-speech tagging and named-entity
segmentation.

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