  We consider a multilingual weakly supervised learning scenario where
knowledge from annotated corpora in a resource-rich language is transferred via
bitext to guide the learning in other languages. Past approaches project labels
across bitext and use them as features or gold labels for training. We propose
a new method that projects model expectations rather than labels, which
facilities transfer of model uncertainty across language boundaries. We encode
expectations as constraints and train a discriminative CRF model using
Generalized Expectation Criteria (Mann and McCallum, 2010). Evaluated on
standard Chinese-English and German-English NER datasets, our method
demonstrates F1 scores of 64% and 60% when no labeled data is used. Attaining
the same accuracy with supervised CRFs requires 12k and 1.5k labeled sentences.
Furthermore, when combined with labeled examples, our method yields significant
improvements over state-of-the-art supervised methods, achieving best reported
numbers to date on Chinese OntoNotes and German CoNLL-03 datasets.
