Dual-Layer Meta-Learning for Few-Shot Named Entity Recognition

We propose a Dual-Layer Meta-Learning Network for Few-Shot Named Entity Recognition, where the network can selectively retain positive training signals from the memory chain to enhance the meta-model's learning capability and filter out interference from non-positive signals. Additionally, to mitigate the parameter explosion caused by the dual-layer network, we further use Chebyshev polynomials to fit the token classification function for entity span detection and employ the Kolmogorov-Arnold Network to fit the prototype-oriented classification function for entity span classification. This effectively reduces the runtime and GPU usage of the dual-layer structure.