  Few-shot named entity recognition (NER) systems aims at recognizing new
classes of entities based on a few labeled samples. A significant challenge in
the few-shot regime is prone to overfitting than the tasks with abundant
samples. The heavy overfitting in few-shot learning is mainly led by spurious
correlation caused by the few samples selection bias. To alleviate the problem
of the spurious correlation in the few-shot NER, in this paper, we propose a
causal intervention-based few-shot NER method. Based on the prototypical
network, the method intervenes in the context and prototype via backdoor
adjustment during training. In particular, intervening in the context of the
one-shot scenario is very difficult, so we intervene in the prototype via
incremental learning, which can also avoid catastrophic forgetting. Our
experiments on different benchmarks show that our approach achieves new
state-of-the-art results (achieving up to 29% absolute improvement and 12% on
average for all tasks).
