Cross-lingual Pseudo-Projected Expectation Regularization for Weakly Supervised Learning

Understand

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).