Causal Intervention-based Few-Shot Named Entity Recognition (2023)

Abstract: Few-shot named entity recognition (NER) systems aim to recognize new classes of entities with limited labeled samples. However, these systems face a significant challenge of overfitting compared to tasks with abundant samples. This overfitting is mainly caused by the spurious correlation resulting from the bias in selecting a few samples. To address this issue, we propose a causal intervention-based few-shot NER method in this paper. Our method, based on the prototypical network, intervenes in the context to block the backdoor path between context and label. In the one-shot scenario, where no additional context is available for intervention, we employ incremental learning to intervene on the prototype, which also helps mitigate catastrophic forgetting. Our experiments on various benchmarks demonstrate that our approach achieves new state-of-the-art results.

## introduction named entity recognition (ner) is a fundamental task in information extraction, which involves identifying and classifying named entities in unstructured text. several methods @xcite @xcite have been developed to achieve efficient results in ner. in practical applications, few-shot named entity recognition has gained significant attention due to challenges in label collection and the high cost of manual labeling. recent years have witnessed numerous studies focusing on few-shot ner. the current approaches primarily revolve around metric learning models, particularly prototypical networks. these models compute a prototype representation @xcite for each class and assign labels to query samples based on their distance to the prototypes of each class @xcite ; yang * corresponding author. ## methods we give a few shot named entity recognition task descriptions in appendix a. in the subsequent sections, we outline our method for few-shot named entity recognition. ## conclusion and future work in this paper, we propose a method for fewshot named entity recognition that addresses the problem of overfitting by using causal interventions. we introduce entity detection, context-based and prototype-based interventions, and sample reweighting to improve performance. our experiments show significant improvements in handling few-shot scenarios and transferring to new domains. future work includes exploring dimensionality reduction techniques to reduce memory usage.