Label Semantics for Few Shot Named Entity Recognition

Jie Ma, Miguel Ballesteros, Srikanth Doss, Rishita Anubhai, Sunil Mallya, Yaser Al-Onaizan, Dan Roth


Abstract
We study the problem of few shot learning for named entity recognition. Specifically, we leverage the semantic information in the names of the labels as a way of giving the model additional signal and enriched priors. We propose a neural architecture that consists of two BERT encoders, one to encode the document and its tokens and another one to encode each of the labels in natural language format. Our model learns to match the representations of named entities computed by the first encoder with label representations computed by the second encoder. The label semantics signal is shown to support improved state-of-the-art results in multiple few shot NER benchmarks and on-par performance in standard benchmarks. Our model is especially effective in low resource settings.
Anthology ID:
2022.findings-acl.155
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1956–1971
Language:
URL:
https://aclanthology.org/2022.findings-acl.155
DOI:
10.18653/v1/2022.findings-acl.155
Bibkey:
Cite (ACL):
Jie Ma, Miguel Ballesteros, Srikanth Doss, Rishita Anubhai, Sunil Mallya, Yaser Al-Onaizan, and Dan Roth. 2022. Label Semantics for Few Shot Named Entity Recognition. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1956–1971, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Label Semantics for Few Shot Named Entity Recognition (Ma et al., Findings 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.findings-acl.155.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2022.findings-acl.155.mp4
Data
CoNLL-2003NCBI DiseaseWNUT 2017