@inproceedings{chen-etal-2023-prompt,
title = "Prompt-Based Metric Learning for Few-Shot {NER}",
author = "Chen, Yanru and
Zheng, Yanan and
Yang, Zhilin",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.451",
doi = "10.18653/v1/2023.findings-acl.451",
pages = "7199--7212",
abstract = "Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples. Existing metric learning methods compute token-level similarities between query and support sets, but are not able to fully incorporate label semantics into modeling. To address this issue, we propose a simple method to largely improve metric learning for NER: 1) multiple prompt schemas are designed to enhance label semantics; 2) we propose a novel architecture to effectively combine multiple prompt-based representations. Empirically, our method achieves new state-of-the-art (SOTA) results under 16 of the 18 considered settings, substantially outperforming the previous SOTA by an average of 9.12{\%} and a maximum of 34.51{\%} in relative gains of micro F1.",
}
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%0 Conference Proceedings
%T Prompt-Based Metric Learning for Few-Shot NER
%A Chen, Yanru
%A Zheng, Yanan
%A Yang, Zhilin
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F chen-etal-2023-prompt
%X Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples. Existing metric learning methods compute token-level similarities between query and support sets, but are not able to fully incorporate label semantics into modeling. To address this issue, we propose a simple method to largely improve metric learning for NER: 1) multiple prompt schemas are designed to enhance label semantics; 2) we propose a novel architecture to effectively combine multiple prompt-based representations. Empirically, our method achieves new state-of-the-art (SOTA) results under 16 of the 18 considered settings, substantially outperforming the previous SOTA by an average of 9.12% and a maximum of 34.51% in relative gains of micro F1.
%R 10.18653/v1/2023.findings-acl.451
%U https://aclanthology.org/2023.findings-acl.451
%U https://doi.org/10.18653/v1/2023.findings-acl.451
%P 7199-7212
Markdown (Informal)
[Prompt-Based Metric Learning for Few-Shot NER](https://aclanthology.org/2023.findings-acl.451) (Chen et al., Findings 2023)
ACL
- Yanru Chen, Yanan Zheng, and Zhilin Yang. 2023. Prompt-Based Metric Learning for Few-Shot NER. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7199–7212, Toronto, Canada. Association for Computational Linguistics.