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        <title>Causal Intervention-based Few-Shot Named Entity Recognition</title>
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        <namePart type="given">Zhen</namePart>
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            <namePart type="given">Juan</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.1046</identifier>
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%0 Conference Proceedings
%T Causal Intervention-based Few-Shot Named Entity Recognition
%A Yang, Zhen
%A Liu, Yongbin
%A Ouyang, Chunping
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yang-etal-2023-causal
%X 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.
%R 10.18653/v1/2023.findings-emnlp.1046
%U https://aclanthology.org/2023.findings-emnlp.1046
%U https://doi.org/10.18653/v1/2023.findings-emnlp.1046
%P 15635-15646

Markdown (Informal)

[Causal Intervention-based Few-Shot Named Entity Recognition](https://aclanthology.org/2023.findings-emnlp.1046) (Yang et al., Findings 2023)

• Causal Intervention-based Few-Shot Named Entity Recognition (Yang et al., Findings 2023)

ACL

• Zhen Yang, Yongbin Liu, and Chunping Ouyang. 2023. Causal Intervention-based Few-Shot Named Entity Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15635–15646, Singapore. Association for Computational Linguistics.