Dual-Contrastive Learning for Low-Resource Cross-Lingual NER Performance

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

How does the integration of dual-contrastive learning frameworks affect the performance of low-resource cross-lingual NER models in terms of F1 score and generalization across different language pairs?

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