CNNBiF: CNN-based Bigram Features for Named Entity Recognition

Chul Sung, Vaibhava Goel, Etienne Marcheret, Steven Rennie, David Nahamoo


Abstract
Transformer models fine-tuned with a sequence labeling objective have become the dominant choice for named entity recognition tasks. However, a self-attention mechanism with unconstrained length can fail to fully capture local dependencies, particularly when training data is limited. In this paper, we propose a novel joint training objective which better captures the semantics of words corresponding to the same entity. By augmenting the training objective with a group-consistency loss component we enhance our ability to capture local dependencies while still enjoying the advantages of the unconstrained self-attention mechanism. On the CoNLL2003 dataset, our method achieves a test F1 of 93.98 with a single transformer model. More importantly our fine-tuned CoNLL2003 model displays significant gains in generalization to out of domain datasets: on the OntoNotes subset we achieve an F1 of 72.67 which is 0.49 points absolute better than the baseline, and on the WNUT16 set an F1 of 68.22 which is a gain of 0.48 points. Furthermore, on the WNUT17 dataset we achieve an F1 of 55.85, yielding a 2.92 point absolute improvement.
Anthology ID:
2021.findings-emnlp.87
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1016–1021
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.87
DOI:
10.18653/v1/2021.findings-emnlp.87
Bibkey:
Cite (ACL):
Chul Sung, Vaibhava Goel, Etienne Marcheret, Steven Rennie, and David Nahamoo. 2021. CNNBiF: CNN-based Bigram Features for Named Entity Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1016–1021, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
CNNBiF: CNN-based Bigram Features for Named Entity Recognition (Sung et al., Findings 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.findings-emnlp.87.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2021.findings-emnlp.87.mp4
Data
CoNLL-2003