  Named Entity Recognition (NER) is the task of identifying and classifying
named entities in large-scale texts into predefined classes. NER in French and
other relatively limited-resource languages cannot always benefit from
approaches proposed for languages like English due to a dearth of large, robust
datasets. In this paper, we present our work that aims to mitigate the effects
of this dearth of large, labeled datasets. We propose a Transformer-based NER
approach for French, using adversarial adaptation to similar domain or general
corpora to improve feature extraction and enable better generalization. Our
approach allows learning better features using large-scale unlabeled corpora
from the same domain or mixed domains to introduce more variations during
training and reduce overfitting. Experimental results on three labeled datasets
show that our adaptation framework outperforms the corresponding non-adaptive
models for various combinations of Transformer models, source datasets, and
target corpora. We also show that adversarial adaptation to large-scale
unlabeled corpora can help mitigate the performance dip incurred on using
Transformer models pre-trained on smaller corpora.
