  Prompt learning is a new paradigm for utilizing pre-trained language models
and has achieved great success in many tasks. To adopt prompt learning in the
NER task, two kinds of methods have been explored from a pair of symmetric
perspectives, populating the template by enumerating spans to predict their
entity types or constructing type-specific prompts to locate entities. However,
these methods not only require a multi-round prompting manner with a high time
overhead and computational cost, but also require elaborate prompt templates,
that are difficult to apply in practical scenarios. In this paper, we unify
entity locating and entity typing into prompt learning, and design a dual-slot
multi-prompt template with the position slot and type slot to prompt locating
and typing respectively. Multiple prompts can be input to the model
simultaneously, and then the model extracts all entities by parallel
predictions on the slots. To assign labels for the slots during training, we
design a dynamic template filling mechanism that uses the extended bipartite
graph matching between prompts and the ground-truth entities. We conduct
experiments in various settings, including resource-rich flat and nested NER
datasets and low-resource in-domain and cross-domain datasets. Experimental
results show that the proposed model achieves a significant performance
improvement, especially in the cross-domain few-shot setting, which outperforms
the state-of-the-art model by +7.7% on average.
