  Cross-lingual named entity recognition (NER) suffers from data scarcity in
the target languages, especially under zero-shot settings. Existing
translate-train or knowledge distillation methods attempt to bridge the
language gap, but often introduce a high level of noise. To solve this problem,
consistency training methods regularize the model to be robust towards
perturbations on data or hidden states. However, such methods are likely to
violate the consistency hypothesis, or mainly focus on coarse-grain
consistency. We propose ConNER as a novel consistency training framework for
cross-lingual NER, which comprises of: (1) translation-based consistency
training on unlabeled target-language data, and (2) dropoutbased consistency
training on labeled source-language data. ConNER effectively leverages
unlabeled target-language data and alleviates overfitting on the source
language to enhance the cross-lingual adaptability. Experimental results show
our ConNER achieves consistent improvement over various baseline methods.
