  Prior works in cross-lingual named entity recognition (NER) with no/little
labeled data fall into two primary categories: model transfer based and data
transfer based methods. In this paper we find that both method types can
complement each other, in the sense that, the former can exploit context
information via language-independent features but sees no task-specific
information in the target language; while the latter generally generates pseudo
target-language training data via translation but its exploitation of context
information is weakened by inaccurate translations. Moreover, prior works
rarely leverage unlabeled data in the target language, which can be
effortlessly collected and potentially contains valuable information for
improved results. To handle both problems, we propose a novel approach termed
UniTrans to Unify both model and data Transfer for cross-lingual NER, and
furthermore, to leverage the available information from unlabeled
target-language data via enhanced knowledge distillation. We evaluate our
proposed UniTrans over 4 target languages on benchmark datasets. Our
experimental results show that it substantially outperforms the existing
state-of-the-art methods.
