Causal Intervention-based Few-Shot Named Entity Recognition

Zhen Yang, Yongbin Liu, Chunping Ouyang

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Submission Track 2: Interpretability, Interactivity, and Analysis of Models for NLP

Keywords: Causal Intervention, Few-Shot Learning, Named Entity Recognition

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.

Submission Number: 3714