A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition

Yuxuan Chen, Jonas Mikkelsen, Arne Binder, Christoph Alt, Leonhard Hennig


Abstract
Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain data. However, their performance in low-resource scenarios, where such data is not available, remains an open question. We introduce an encoder evaluation framework, and use it to systematically compare the performance of state-of-the-art pre-trained representations on the task of low-resource NER. We analyze a wide range of encoders pre-trained with different strategies, model architectures, intermediate-task fine-tuning, and contrastive learning. Our experimental results across ten benchmark NER datasets in English and German show that encoder performance varies significantly, suggesting that the choice of encoder for a specific low-resource scenario needs to be carefully evaluated.
Anthology ID:
2022.repl4nlp-1.6
Volume:
Proceedings of the 7th Workshop on Representation Learning for NLP
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | RepL4NLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
46–59
Language:
URL:
https://aclanthology.org/2022.repl4nlp-1.6
DOI:
10.18653/v1/2022.repl4nlp-1.6
Bibkey:
Cite (ACL):
Yuxuan Chen, Jonas Mikkelsen, Arne Binder, Christoph Alt, and Leonhard Hennig. 2022. A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition. In Proceedings of the 7th Workshop on Representation Learning for NLP, pages 46–59, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition (Chen et al., RepL4NLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.repl4nlp-1.6.pdf
Code
 dfki-nlp/fewie
Data
CoNLL-2003Few-NERDWNUT 2017