LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting

Understand

Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data.

• Existing dominant approaches usually suffer from the challenge that the target domain has different label sets compared with a resource-rich source domain, which can be concluded as class transfer and domain transfer.
• In this paper, we propose a lightweight tuning paradigm for low-resource NER via pluggable prompting (LightNER).
• Specifically, we construct the unified learnable verbalizer of entity categories to generate the entity span sequence and entity categories without any label-specific classifiers, thus addressing the class transfer issue.