  Cross-lingual Named Entity Recognition (NER) has recently become a research
hotspot because it can alleviate the data-hungry problem for low-resource
languages. However, few researches have focused on the scenario where the
source-language labeled data is also limited in some specific domains. A common
approach for this scenario is to generate more training data through
translation or generation-based data augmentation method. Unfortunately, we
find that simply combining source-language data and the corresponding
translation cannot fully exploit the translated data and the improvements
obtained are somewhat limited. In this paper, we describe our novel
dual-contrastive framework ConCNER for cross-lingual NER under the scenario of
limited source-language labeled data. Specifically, based on the
source-language samples and their translations, we design two contrastive
objectives for cross-language NER at different grammatical levels, namely
Translation Contrastive Learning (TCL) to close sentence representations
between translated sentence pairs and Label Contrastive Learning (LCL) to close
token representations within the same labels. Furthermore, we utilize knowledge
distillation method where the NER model trained above is used as the teacher to
train a student model on unlabeled target-language data to better fit the
target language. We conduct extensive experiments on a wide variety of target
languages, and the results demonstrate that ConCNER tends to outperform
multiple baseline methods. For reproducibility, our code for this paper is
available at https://github.com/GKLMIP/ConCNER.
