CNNBiF: CNN-based Bigram Features for Named Entity Recognition
Chul Sung, Vaibhava Goel, Etienne Marcheret, Steven J. Rennie, D. Nahamoo · 2021
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.