Robustness of Dual-Contrastive Cross-Lingual NER Models Under Limited Source-Language Labeled Data Constraints

Abstract

Abstract: 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 obtaine

Research Question

What is the impact of limited source-language labeled data on the robustness of dual-contrastive cross-lingual NER models across diverse low-resource domains?

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