Cross-lingual named entity recognition (NER) aims to train an NER model for
the target language leveraging only labeled source language data and unlabeled
target language data. Prior approaches either perform label projection on
translated source language data or employ a source model to assign pseudo
labels for target language data and train a target model on these
pseudo-labeled data to generalize to the target language. However, these
automatic labeling procedures inevitably introduce noisy labels, thus leading
to a performance drop. In this paper, we propose a Global-Local Denoising
framework (GLoDe) for cross-lingual NER. Specifically, GLoDe introduces a
progressive denoising strategy to rectify incorrect pseudo labels by leveraging
both global and local distribution information in the semantic space. The
refined pseudo-labeled target language data significantly improves the model's
generalization ability. Moreover, previous methods only consider improving the
model with language-agnostic features, however, we argue that target
language-specific features are also important and should never be ignored. To
this end, we employ a simple auxiliary task to achieve this goal. Experimental
results on two benchmark datasets with six target languages demonstrate that
our proposed GLoDe significantly outperforms current state-of-the-art methods.

跨语言命名实体识别领域的一项研究，提出了一种全局 - 局部去噪框架（GLoDe），通过利用语义空间中的全局和局部分布信息，改善了模型的泛化能力。在两个基准数据集上的实验结果表明，GLoDe 显著优于目前最先进的方法。