Few-shot Named Entity Recognition via encoder and class intervention

In the real world, the large and complex nature of text increases the difficulty of tagging and results in a limited amount of tagged text. Few-shot Named Entity Recognition(NER) only uses a small amount of annotation data to identify and classify entities. It avoids the above problems. Few-shot learning methods usually use prior knowledge to achieve good results. However, prior knowledge may become a confounding factor affecting the relation between sample features and real labels. This problem leads to bias and difficulty accurately capturing class. To solve this problem, a new model, Few-shot Named Entity Recognition via Encoder and Class Intervention, is proposed based on causality. We show that we can steer the model to manufacture interventions on encoder and class, and reduce the interference of confounding factors. Specifically, while cross-sample attention perturbation is used in the encoder layer, a practical causal relation between feature and classification label is developed in the class layer. This way is an attempt of causal methodology in the Few-shot Named Entity Recognition task, which improves the discrimination ability of the NER classifier. Experimental results demonstrate that our model outperforms baseline models in both 5-way and 10-way on two NER datasets.