  We present DualNER, a simple and effective framework to make full use of both
annotated source language corpus and unlabeled target language text for
zero-shot cross-lingual named entity recognition (NER). In particular, we
combine two complementary learning paradigms of NER, i.e., sequence labeling
and span prediction, into a unified multi-task framework. After obtaining a
sufficient NER model trained on the source data, we further train it on the
target data in a {\it dual-teaching} manner, in which the pseudo-labels for one
task are constructed from the prediction of the other task. Moreover, based on
the span prediction, an entity-aware regularization is proposed to enhance the
intrinsic cross-lingual alignment between the same entities in different
languages. Experiments and analysis demonstrate the effectiveness of our
DualNER. Code is available at https://github.com/lemon0830/dualNER.
