  Existing models for cross-domain named entity recognition (NER) rely on
numerous unlabeled corpus or labeled NER training data in target domains.
However, collecting data for low-resource target domains is not only expensive
but also time-consuming. Hence, we propose a cross-domain NER model that does
not use any external resources. We first introduce a Multi-Task Learning (MTL)
by adding a new objective function to detect whether tokens are named entities
or not. We then introduce a framework called Mixture of Entity Experts (MoEE)
to improve the robustness for zero-resource domain adaptation. Finally,
experimental results show that our model outperforms strong unsupervised
cross-domain sequence labeling models, and the performance of our model is
close to that of the state-of-the-art model which leverages extensive
resources.
