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Cross-lingual Projected Expectation Regularization for Weakly Supervised Learning
2014
Transactions of the Association for Computational Linguistics
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
doi:10.1162/tacl_a_00165
fatcat:j2xxjxjjtvhxbg2aqcs2gejzxq