Comparison of Adapter-Based and Full Model Fine-Tuning for Cross-Lingual NER in Low-Resource Languages

Abstract

Abstract: Named Entity Recognition (NER) is a crucial task in natural language processing that enables machines to better understand human languages. The multilingual NER task is challenging due to the significant syntactic and lexical variations across languages. While large pre-trained models like XLM-R have shown effectiveness on multilingual NER, they require substantial computational resources. Parameter-Efficient finetuning (PEFT) methods have recently gained attention as they can achieve comparable performance to full fine-tuning while using only a fraction of the parameters. In this study, we ev

Research Question

How does adapter-based fine-tuning compare to full model fine-tuning in cross-lingual NER accuracy on low-resource languages within the WikiAnn benchmark?

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