2017, after word-level adversarial training, we also adopt a refinement step to construct a bilingual dictionary for the top-k most frequent words in the source language 3 based on V s and V t , and further optimize U with Equation 2 in a supervised way.
once s is projected into the same semantic space as t, we can regard both sentences as coming from one unified language and directly project annotations from s to t. name tagging not only relies on word level features, but also on sequential contextual features for entity type classification.